1work_id,doi,arxiv_id,title,source_name,year,type,primary_category,n_categories,c12,c36,ncs36,cited_by_count,fwci,has_abstract,is_oa,period,score,set,pi_review22949117887,10.57702/o9raffed,,Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift,arXiv (Cornell University),2024,preprint,computer_vision,3,14211.0,14214.0,5890.544305226324,14214,,True,True,post,5890.544305226324,landmark,True33118608800,10.57702/zp44cu3g,,Learning Multiple Layers of Features from Tiny Images,,2024,article,computer_vision,3,23325.0,23341.0,2765.184913506997,23395,559.7906,True,True,post,2765.184913506997,landmark,True44320013936,10.1007/978-3-658-40442-0_9,,Generative Adversarial Nets,Springer eBooks,2023,book-chapter,computer_vision,3,17934.0,19230.0,2673.891459493373,19272,1769.2415,False,False,post,2673.891459493373,landmark,True54386076325,10.1109/cvpr52729.2023.00721,2207.02696,YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors,IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings,2023,conference-paper,computer_vision,5,6781.0,10586.0,1477.2917574868547,12390,1387.4265,True,True,baseline,1477.2917574868547,landmark,True63119786062,,,An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale,International Conference on Learning Representations,2021,conference-paper,computer_vision,5,4071.0,9465.0,1181.820775699879,449,38.1179,True,False,baseline,1181.820775699879,landmark,True73138516171,10.1109/iccv48922.2021.00986,2103.14030,Swin Transformer: Hierarchical Vision Transformer using Shifted Windows,IEEE/CVF International Conference on Computer Vision (ICCV),2021,conference-paper,computer_vision,6,3057.0,9391.0,1172.5809724878566,33262,1556.4216,True,True,baseline,1172.5809724878566,landmark,True84390874575,10.1109/iccv51070.2023.00371,,Segment Anything,IEEE/CVF International Conference on Computer Vision (ICCV),2023,conference-paper,computer_vision,5,3127.0,7864.0,1097.4326828713986,10910,1240.4061,True,False,post,1097.4326828713986,landmark,True94402754006,10.1109/cvpr52733.2024.01605,,DETRs Beat YOLOs on Real-time Object Detection,IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings,2024,conference-paper,computer_vision,4,2662.0,4773.0,1001.7389274520061,4548,991.256,True,False,post,1001.7389274520061,landmark,True104403770406,10.1007/978-3-031-72751-1_1,2402.13616,YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information,Lecture notes in computer science,2024,conference-paper,computer_vision,3,3072.0,4486.0,941.504468583637,4486,979.4658,False,True,post,941.504468583637,landmark,True114312933868,10.1109/cvpr52688.2022.01042,,High-Resolution Image Synthesis with Latent Diffusion Models,IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings,2022,conference-paper,computer_vision,3,1866.0,6719.0,907.833780459159,16051,1184.1165,True,False,baseline,907.833780459159,landmark,True122963420686,10.1109/tpami.2019.2913372,,Squeeze-and-Excitation Networks,IEEE Transactions on Pattern Analysis and Machine Intelligence,2019,article,computer_vision,3,2975.0,5976.0,905.785826797903,12104,843.0736,True,False,baseline,905.785826797903,landmark,True134226002507,10.1109/cvpr52688.2022.01553,,Masked Autoencoders Are Scalable Vision Learners,IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings,2022,conference-paper,computer_vision,4,1906.0,4278.0,849.9419256281733,7977,974.4316,True,True,baseline,849.9419256281733,landmark,True144399300671,10.1002/mp.17191,1912.01703,The effects of back‐projection variants in BPF‐like TOF PET reconstruction using CNN filtration – Based on simulated and clinical brain data,Medical Physics,2024,article,computer_vision,5,14860.0,14915.0,822.2465127032651,0,0.0,True,True,baseline,822.2465127032651,landmark,True157106270182,10.82419/203,,Bridging the Gap between Object and Image-level Representations for Open-Vocabulary Detection,MBZUAI iRep,2022,dissertation,computer_vision,4,24.0,53.0,720.1328671328672,0,,True,True,baseline,720.1328671328672,landmark,True164415795831,10.52202/079017-3429,2405.14458,YOLOv10: Real-Time End-to-End Object Detection,neural information processing systems,2024,conference-paper,computer_vision,4,1157.0,2788.0,585.1347432927286,2788,608.7273,True,True,post,585.1347432927286,landmark,True173035524453,10.1109/cvpr42600.2020.00975,1911.05722,Momentum Contrast for Unsupervised Visual Representation Learning,IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings,2020,conference-paper,computer_vision,4,1903.0,3802.0,538.8557516528527,12652,650.908,True,True,baseline,538.8557516528527,landmark,True184321232185,10.1007/978-3-031-25066-8_9,2105.05537,Swin-Unet: Unet-Like Pure Transformer for Medical Image Segmentation,Lecture notes in computer science,2023,conference-paper,computer_vision,3,2204.0,3841.0,536.0171585591356,4853,526.4534,False,True,baseline,536.0171585591356,landmark,True193005680577,10.48550/arxiv.2002.05709,2002.05709,A Simple Framework for Contrastive Learning of Visual Representations,arXiv (Cornell University),2020,preprint,computer_vision,5,1836.0,3360.0,523.1590380139643,6894,,True,True,baseline,523.1590380139643,landmark,True203182555022,,,Generative Adversarial Networks for Semantic Image Synthesis and Unconditional Synthesis with Limited Data,bonndoc (University of Bonn),2024,dissertation,computer_vision,3,45.0,47.0,515.6,47,,True,True,post,515.6,landmark,True213152382703,,,EVOLUTION OF YOLO ALGORITHM AND YOLOV5: THE STATE-OF-THE-ART OBJECT DETECTION ALGORITHM,,2021,dissertation,computer_vision,2,51.0,80.0,513.8375350140057,97,,False,False,baseline,513.8375350140057,landmark,True224312443924,10.1109/cvpr52688.2022.01167,,A ConvNet for the 2020s,IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings,2022,conference-paper,computer_vision,3,1653.0,3671.0,496.0050317109053,8300,604.8545,True,False,baseline,496.0050317109053,landmark,True234300424419,10.4230/lipics.giscience.2025.18,,U-Prithvi: Integrating a Foundation Model and U-Net for Enhanced Flood Inundation Mapping,Leibniz international proceedings in informatics,2025,conference-paper,computer_vision,3,977.0,977.0,144.34954954954955,977,1317.4998,True,True,post,494.61490121304024,landmark,True242335728318,10.57702/e7wwo0zk,,Reading digits in natural images with unsupervised feature learning,,2024,article,computer_vision,1,4147.0,4149.0,491.5278782460276,4149,55.5129,True,True,post,491.5278782460276,landmark,True254390873054,10.1109/iccv51070.2023.00355,,Adding Conditional Control to Text-to-Image Diffusion Models,IEEE/CVF International Conference on Computer Vision (ICCV),2023,conference-paper,computer_vision,6,1511.0,3376.0,471.1257295744967,4408,501.2109,True,False,post,471.1257295744967,landmark,True263100507239,,,Illumination-Adaptive Person Re-identification,Institutional Repositories DataBase (IRDB),2020,dissertation,computer_vision,1,44.0,66.0,447.55516014234877,97,,True,True,baseline,447.55516014234877,landmark,True272626778328,10.65215/2q58a426,1706.03762,Attention Is All You Need,,2025,preprint,nlp,5,24486.0,24486.0,29646.72006980803,24487,,True,True,post,12396.254320473392,landmark,True282963341956,10.18653/v1/n19-1423,,BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT),2019,conference-paper,nlp,3,9645.0,19296.0,2566.536785186562,31944,2211.5894,True,True,baseline,2566.536785186562,landmark,True293098044990,,,Traduction Non Supervisée de Langages de Programmation,theses.fr (ABES),2022,dissertation,nlp,4,121.0,143.0,1240.408521303258,162,,True,True,baseline,1240.408521303258,landmark,True302896457183,10.4230/lipics.cosit.2022.18,1810.04805,AI-Assisted Pipeline for Dynamic Generation of Trustworthy Health Supplement Content at Scale,arXiv (Cornell University),2018,preprint,nlp,2,1630.0,5855.0,902.4576906598114,44705,,True,True,baseline,902.4576906598114,landmark,True312787560479,10.18653/v1/n18-1202,1802.05365,Deep Contextualized Word Representations,Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT),2018,conference-paper,nlp,2,2252.0,4791.0,741.8627823725466,11541,762.8608,True,True,baseline,741.8627823725466,landmark,True324411403346,10.1145/3744746,2307.06435,A Comprehensive Overview of Large Language Models,ACM Transactions on Intelligent Systems and Technology,2025,article,nlp,5,1097.0,1097.0,373.692295245369,1097,382.0017,True,True,post,555.3659637980605,landmark,True334224308101,10.48550/arxiv.2204.02311,2204.02311,PaLM: Scaling Language Modeling with Pathways,arXiv (Cornell University),2022,preprint,nlp,7,950.0,1657.0,481.54051809067556,2051,,True,True,baseline,481.54051809067556,landmark,True342979736514,10.13025/17598,,Neural transfer learning for natural language processing,Arrow@dit (Dublin Institute of Technology),2019,dissertation,nlp,5,62.0,116.0,422.42293906810033,177,,True,True,baseline,422.42293906810033,landmark,True354386506836,10.1515/9781474471459-050,,5.7 The WORDNET lexical database of English,Edinburgh University Press eBooks,2019,book-chapter,nlp,1,1138.0,1337.0,415.89661853577525,1865,230.3576,False,False,baseline,415.89661853577525,landmark,True362982399380,10.18653/v1/2020.acl-main.703,1910.13461,"BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension",Annual Meeting of the Association for Computational Linguistics (ACL),2020,conference-paper,nlp,5,1454.0,2915.0,413.1416402072767,8244,528.5478,True,True,baseline,413.1416402072767,landmark,True377125799258,10.1109/tpami.2026.3657354,2312.12148,Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment,IEEE Transactions on Pattern Analysis and Machine Intelligence,2026,article,nlp,4,115.0,115.0,127.97217616701306,115,191.4688,True,True,post,408.85036662226526,landmark,True384391136507,10.1145/3641289,2307.03109,A Survey on Evaluation of Large Language Models,ACM Transactions on Intelligent Systems and Technology,2024,article,nlp,5,2160.0,3004.0,373.1207027772732,3004,440.36,True,True,post,373.1207027772732,landmark,True392979826702,10.18653/v1/2020.emnlp-demos.6,,Transformers: State-of-the-Art Natural Language Processing,Conference on Empirical Methods in Natural Language Processing: System Demonstrations (EMNLP),2020,conference-paper,nlp,3,965.0,2605.0,369.2054794991272,8292,488.3322,True,True,baseline,369.2054794991272,landmark,True404404534210,10.1145/3703155,2311.05232,"A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions",ACM Transactions on Information Systems,2024,article,nlp,4,1138.0,2379.0,295.4907296628272,2379,354.0441,True,True,post,295.4907296628272,landmark,True412983040767,10.18653/v1/2020.acl-main.747,1911.02116,Unsupervised Cross-lingual Representation Learning at Scale,Annual Meeting of the Association for Computational Linguistics (ACL),2020,conference-paper,nlp,3,1179.0,2047.0,290.1203902244581,5335,332.4086,True,True,baseline,290.1203902244581,landmark,True422948223045,10.18653/v1/p19-1355,1906.02243,Energy and Policy Considerations for Deep Learning in NLP,Annual Meeting of the Association for Computational Linguistics (ACL),2019,conference-paper,nlp,4,368.0,863.0,257.5756075430014,3453,514.1231,True,True,baseline,257.5756075430014,landmark,True433044864684,,,Leveraging orthographic information to improve machine translation of under-resourced languages,,2020,dissertation,nlp,3,52.0,53.0,244.74101247248717,58,,False,False,baseline,244.74101247248717,landmark,True444404782964,10.18653/v1/2024.emnlp-main.64,,A Survey on In-context Learning,Conference on Empirical Methods in Natural Language Processing (EMNLP),2024,conference-paper,nlp,3,646.0,879.0,244.3285362084626,632,188.3078,True,True,post,244.3285362084626,landmark,True454221142221,10.48550/arxiv.2203.05794,2203.05794,BERTopic: Neural topic modeling with a class-based TF-IDF procedure,arXiv (Cornell University),2022,preprint,nlp,3,416.0,812.0,235.9751965537891,1342,,True,True,baseline,235.9751965537891,landmark,True463169483174,10.18653/v1/2021.naacl-main.41,,mT5: A Massively Multilingual Pre-trained Text-to-Text Transformer,Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT),2021,conference-paper,nlp,4,642.0,1458.0,231.16193233956648,1621,144.7472,True,True,baseline,231.16193233956648,landmark,True473196350727,10.4230/oasics.ldk.2021.34,,"HISTORIAE, History of Socio-Cultural Transformation as Linguistic Data Science. A Humanities Use Case",DROPS (Schloss Dagstuhl – Leibniz Center for Informatics),2021,article,nlp,1,8042.0,12201.0,226.99110878661088,0,0.0,True,True,baseline,226.99110878661088,landmark,True484229005866,10.4230/oasics.slate.2022.19,2205.01068,Analysing Off-The-Shelf Options for Question Answering with Portuguese FAQs,arXiv (Cornell University),2022,preprint,nlp,5,473.0,740.0,215.05128749975853,879,,True,True,baseline,215.05128749975853,landmark,True497130436101,10.1038/s41586-025-10097-9,2506.20430,An agentic system for rare disease diagnosis with traceable reasoning,Nature,2026,article,nlp,7,59.0,59.0,26.859760394644116,59,109.0609,True,True,post,209.7580141801187,landmark,True502798812533,10.18653/v1/p18-1031,1801.06146,Universal Language Model Fine-tuning for Text Classification,Annual Meeting of the Association for Computational Linguistics (ACL),2018,conference-paper,nlp,6,617.0,1344.0,208.11178866806569,3882,191.3134,True,True,baseline,208.11178866806569,landmark,True513156636935,10.18653/v1/2021.emnlp-main.552,2104.08821,SimCSE: Simple Contrastive Learning of Sentence Embeddings,Conference on Empirical Methods in Natural Language Processing (EMNLP),2021,conference-paper,nlp,5,480.0,1288.0,204.20889496115336,2686,230.7564,True,True,baseline,204.20889496115336,landmark,True522219249508,10.1007/978-3-032-27827-2_35,,Perceptual Signal Analysis and Eigenvalue-Weighted Metrics for Musical Audio Quality Assessment,Lecture notes in computer science,2026,conference-paper,speech_audio,2,844.0,844.0,3046.350724637681,845,0.0,False,True,post,3000.6061689494945,landmark,True531524333225,10.57702/jb3fvbn9,,Kaldi Speech Recognition Toolkit,Infoscience (Ecole Polytechnique Fédérale de Lausanne),2024,article,speech_audio,1,4597.0,4599.0,571.2323941653393,4609,5.5064,True,True,post,571.2323941653393,landmark,True544392233542,10.3390/s24051546,,A Review of Machine Learning Approaches for the Personalization of Amplification in Hearing Aids,Sensors,2024,dissertation,speech_audio,1,7.0,19.0,327.9259259259259,19,,True,True,post,327.9259259259259,landmark,True554311000453,10.48550/arxiv.2212.04356,2212.04356,Robust Speech Recognition via Large-Scale Weak Supervision,arXiv (Cornell University),2022,preprint,speech_audio,5,337.0,759.0,220.57287461123883,1134,,True,True,post,220.57287461123883,landmark,True562936774411,10.21437/interspeech.2019-2680,1904.08779,SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition,Interspeech (USB),2019,conference-paper,speech_audio,5,465.0,1205.0,160.2755403270008,3499,180.2006,True,True,baseline,160.2755403270008,landmark,True574406417959,10.1109/taslpro.2025.3530270,,Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers,IEEE Transactions on Audio Speech and Language Processing,2025,article,speech_audio,2,281.0,281.0,95.7224566672276,122,50.7671,True,False,post,142.25873821992252,landmark,True582952218014,10.1109/taslp.2019.2915167,1809.07454,Conv-TasNet: Surpassing Ideal Time–Frequency Magnitude Masking for Speech Separation,IEEE/ACM Transactions on Audio Speech and Language Processing,2019,article,speech_audio,2,273.0,598.0,126.90333200584095,2042,137.8473,True,True,baseline,126.90333200584095,landmark,True592890964092,10.1109/icassp.2018.8461375,,X-Vectors: Robust DNN Embeddings for Speaker Recognition,IEEE International Conference on Acoustics Speech and Signal Processing,2018,conference-paper,speech_audio,4,274.0,698.0,108.08186643624245,2620,119.8439,True,False,baseline,108.08186643624245,landmark,True603097777922,10.21437/interspeech.2020-3015,2005.08100,Conformer: Convolution-augmented Transformer for Speech Recognition,Interspeech (USB),2020,conference-paper,speech_audio,4,335.0,755.0,107.00581075694473,3070,145.6429,True,True,baseline,107.00581075694473,landmark,True612962866211,10.1109/taslp.2018.2842159,1708.07524,Supervised Speech Separation Based on Deep Learning: An Overview,IEEE/ACM Transactions on Audio Speech and Language Processing,2018,article,speech_audio,3,206.0,429.0,106.2474690299835,1524,120.6683,True,True,baseline,106.2474690299835,landmark,True621530164934,10.7275/a01b-fx07,,A Probabilistic Model of Hierarchical Music Analysis,Scholarworks (University of Massachusetts Amherst),2020,dissertation,speech_audio,1,15.0,15.0,105.97222222222223,36,,True,True,baseline,105.97222222222223,landmark,True632964243274,10.1109/icassp.2018.8461368,1712.05884,Natural TTS Synthesis by Conditioning Wavenet on MEL Spectrogram Predictions,IEEE International Conference on Acoustics Speech and Signal Processing,2018,conference-paper,speech_audio,3,299.0,662.0,102.50744352549069,2692,119.0117,True,True,baseline,102.50744352549069,landmark,True643167533889,10.48550/arxiv.2106.04624,2106.04624,SpeechBrain: A General-Purpose Speech Toolkit,arXiv (Cornell University),2021,preprint,speech_audio,1,129.0,288.0,96.44297133944238,504,,True,True,baseline,96.44297133944238,landmark,True653169320628,10.1109/taslp.2021.3122291,2106.07447,HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units,IEEE/ACM Transactions on Audio Speech and Language Processing,2021,article,speech_audio,5,222.0,765.0,89.15495130101968,2919,124.2084,True,True,baseline,89.15495130101968,landmark,True662975281968,,,"Modeling Music : Studies of Music Transcription, Music Perception and Music Production",KTH Publication Database DiVA (KTH Royal Institute of Technology),2018,dissertation,speech_audio,6,1.0,13.0,87.36363636363636,15,,True,True,baseline,87.36363636363636,landmark,True674372260310,10.1109/icassp49357.2023.10095969,2211.06687,Large-Scale Contrastive Language-Audio Pretraining with Feature Fusion and Keyword-to-Caption Augmentation,IEEE International Conference on Acoustics Speech and Signal Processing,2023,conference-paper,speech_audio,5,124.0,320.0,87.23607346034898,439,97.1083,True,True,post,87.23607346034898,landmark,True684372266552,10.1109/icassp49357.2023.10095889,2206.04769,CLAP Learning Audio Concepts from Natural Language Supervision,IEEE International Conference on Acoustics Speech and Signal Processing,2023,conference-paper,speech_audio,4,134.0,318.0,86.6908480012218,438,96.4447,True,True,baseline,86.6908480012218,landmark,True692900368655,10.1109/icassp.2019.8683855,1811.02508,SDR – Half-baked or Well Done?,IEEE International Conference on Acoustics Speech and Signal Processing,2019,conference-paper,speech_audio,1,151.0,313.0,85.71885565669702,1146,88.5281,True,True,baseline,85.71885565669702,landmark,True703209984917,10.1109/jstsp.2022.3188113,2110.13900,WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing,IEEE Journal of Selected Topics in Signal Processing,2022,article,speech_audio,5,388.0,800.0,82.43168975381538,1897,117.1333,True,True,baseline,82.43168975381538,landmark,True714318351475,10.48550/arxiv.2301.11325,2301.11325,MuChoMusic dataset,arXiv (Cornell University),2023,preprint,speech_audio,4,126.0,164.0,77.2652719665272,182,,True,True,post,77.2652719665272,landmark,True723196974791,10.21437/interspeech.2021-698,,AST: Audio Spectrogram Transformer,Interspeech (USB),2021,conference-paper,speech_audio,2,119.0,308.0,72.25665452507705,1086,83.5514,True,False,baseline,72.25665452507705,landmark,True737135186946,10.1109/jstsp.2026.3673981,,Scalable Fluid Antenna Systems: A New Paradigm for Array Signal Processing,IEEE Journal of Selected Topics in Signal Processing,2026,article,speech_audio,1,20.0,20.0,66.31436314363144,20,82.3333,True,False,post,71.10441158648091,landmark,True742775304348,10.1109/icassp.2018.8462105,1712.01769,State-of-the-Art Speech Recognition with Sequence-to-Sequence Models,IEEE International Conference on Acoustics Speech and Signal Processing,2018,conference-paper,speech_audio,6,240.0,455.0,70.45451178866807,1009,71.1573,True,True,baseline,70.45451178866807,landmark,True753015199127,10.1109/icassp40776.2020.9054266,,Dual-Path RNN: Efficient Long Sequence Modeling for Time-Domain Single-Channel Speech Separation,IEEE International Conference on Acoustics Speech and Signal Processing,2020,conference-paper,speech_audio,2,151.0,293.0,68.6972461420411,780,69.2189,True,False,baseline,68.6972461420411,landmark,True762808631503,10.21437/interspeech.2018-1929,1806.05622,VoxCeleb2: Deep Speaker Recognition,Interspeech (USB),2018,conference-paper,speech_audio,4,149.0,442.0,68.44152573756327,2246,81.0403,True,True,baseline,68.44152573756327,landmark,True777205708264,10.1146/annurev,,Relationship between educational robotics and executive functions in children with neurodevelopmental disorders,HAL (Le Centre pour la Communication Scientifique Directe),2026,conference-paper,robotics,1,117.0,117.0,897.9818032562595,123,157.1634,True,True,post,415.96080778091334,landmark,True784385473486,10.48550/arxiv.2307.15818,2307.15818,RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control,arXiv (Cornell University),2023,preprint,robotics,7,157.0,252.0,151.2,274,,True,True,post,151.2,landmark,True797126053009,10.1016/j.inffus.2026.104193,2508.05342,Information-theoretic graph fusion with vision-language-action model for policy reasoning and dual robotic control,Information Fusion,2026,article,robotics,3,41.0,41.0,45.41643153526971,41,97.2957,False,True,post,145.7640437522859,landmark,True804414079054,10.15607/rss.2025.xxi.010,,π₀: A Vision-Language-Action Flow Model for General Robot Control,,2025,conference-paper,robotics,2,274.0,274.0,243.52116128619693,274,276.3475,True,True,post,138.71492623579633,landmark,True814293774946,10.1007/978-981-19-2980-9_32,,Employing Adaptive Learning and Intelligent Tutoring Robots for Virtual Classrooms and Smart Campuses: Reforming Education in the Age of Artificial Intelligence,Lecture notes in electrical engineering,2022,book-chapter,robotics,1,91.0,175.0,135.97268806899856,262,161.0471,False,False,baseline,135.97268806899856,landmark,True827130402520,10.1109/tro.2026.3666139,2402.13255,How NeRFs and 3-D Gaussian Splatting Are Reshaping SLAM: A Survey,IEEE Transactions on Robotics,2026,article,robotics,3,38.0,38.0,49.59542857142858,38,48.5016,True,True,post,135.09838201431373,landmark,True832963188159,10.15607/rss.2018.xiv.019,1711.00199,PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes,,2018,conference-paper,robotics,4,201.0,451.0,129.2718583898181,2127,284.4153,True,True,baseline,129.2718583898181,landmark,True844398796510,10.1109/tnnls.2025.3650584,2405.14093,A Survey on Vision--Language--Action Models for Embodied AI,IEEE Transactions on Neural Networks and Learning Systems,2026,article,robotics,10,36.0,36.0,96.49320091673033,36,77.9076,True,True,post,127.98794085566564,landmark,True854383097638,10.1109/icra48891.2023.10160591,2209.07753,Code as Policies: Language Model Programs for Embodied Control,Proceedings - IEEE International Conference on Robotics and Automation/Proceedings,2023,conference-paper,robotics,3,237.0,511.0,125.95880564003319,722,324.1052,True,True,post,125.95880564003319,landmark,True864385430679,10.15607/rss.2023.xix.025,2212.06817,RT-1: Robotics Transformer for Real-World Control at Scale,,2023,conference-paper,robotics,10,195.0,488.0,120.28942691259529,689,309.7205,True,True,post,120.28942691259529,landmark,True872990747716,10.1177/0278364919887447,,Learning dexterous in-hand manipulation,The International Journal of Robotics Research,2019,article,robotics,2,490.0,777.0,118.1470865906983,1585,89.792,True,True,baseline,118.1470865906983,landmark,True884383108457,10.1109/icra48891.2023.10161317,2209.11302,ProgPrompt: Generating Situated Robot Task Plans using Large Language Models,Proceedings - IEEE International Conference on Robotics and Automation/Proceedings,2023,conference-paper,robotics,7,245.0,492.0,113.46421800672258,583,109.6395,True,True,baseline,113.46421800672258,landmark,True891581527321,10.7907/2e18-0e29,,Collision Detection and Avoidance in Computer Controlled Manipulators,CaltechTHESIS (California Institute of Technology),2018,dissertation,robotics,1,14.0,16.0,110.6320610687023,276,,True,True,baseline,110.6320610687023,landmark,True903043971245,10.1109/tro.2021.3075644,2007.11898,"ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual–Inertial, and Multimap SLAM",IEEE Transactions on Robotics,2021,article,robotics,2,542.0,1312.0,104.21312513733956,4274,299.5553,True,True,baseline,104.21312513733956,landmark,True914323572061,10.48550/arxiv.2303.03378,2303.03378,PaLM-E: An Embodied Multimodal Language Model,arXiv (Cornell University),2023,preprint,robotics,7,263.0,330.0,103.01438010104935,346,,True,True,post,103.01438010104935,landmark,True923003533476,10.1109/tits.2021.3054625,2002.00444,Deep Reinforcement Learning for Autonomous Driving: A Survey,IEEE Transactions on Intelligent Transportation Systems,2021,article,robotics,3,436.0,870.0,101.39190540115963,2313,122.2676,True,True,baseline,101.39190540115963,landmark,True937140452175,10.1109/tmech.2025.3579247,2501.02116,"Humanoid Locomotion and Manipulation: Current Progress and Challenges in Control, Planning, and Learning",IEEE/ASME Transactions on Mechatronics,2026,article,robotics,3,28.0,28.0,50.20033955857386,28,41.6958,True,True,post,99.54617622107328,landmark,True944385430674,10.15607/rss.2023.xix.016,2304.13705,Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware,,2023,conference-paper,robotics,6,86.0,395.0,97.36541727556381,649,291.7396,True,True,post,97.36541727556381,landmark,True954406918931,10.1186/s44147-025-00582-3,,"UAV swarms: research, challenges, and future directions",Journal of Engineering and Applied Science,2025,article,robotics,1,182.0,182.0,69.22742920177257,182,80.227,True,True,post,92.13911158728078,landmark,True962923943538,10.14711/thesis-991012769067103412,,FIESTA : fast incremental euclidean distance fields for online motion planning of aerial robots,,2019,dissertation,robotics,3,5.0,12.0,91.15966386554621,15,,True,False,baseline,91.15966386554621,landmark,True974385403811,10.15607/rss.2023.xix.026,,Diffusion Policy: Visuomotor Policy Learning via Action Diffusion,,2023,conference-paper,robotics,2,113.0,387.0,89.24929343211716,522,98.6756,False,True,post,89.24929343211716,landmark,True987152514513,10.1016/j.asoc.2026.115169,,Cooperative 4D trajectory planning for multi aerial vehicles combining receding horizon optimization and differential evolution algorithm in dynamic battlefield environment,Applied Soft Computing,2026,article,robotics,3,25.0,25.0,27.82003829717675,25,90.3155,True,True,post,88.88051448310114,landmark,True997123353670,10.1109/tase.2026.3653476,,Trajectory Planning Method for Fracture Reduction of Parallel Robots Based on DMP and APF,IEEE Transactions on Automation Science and Engineering,2026,article,robotics,1,25.0,25.0,27.692946058091287,25,69.497,True,False,post,88.88051448310114,landmark,True1002925329277,10.23919/ecc.2019.8796030,1903.11199,Control Barrier Functions: Theory and Applications,European Control Conference (ECC),2019,conference-paper,robotics,2,90.0,265.0,87.20226791818132,1936,124.0197,True,True,baseline,87.20226791818132,landmark,True1014388761637,10.17760/d20592696,2308.13972,Towards autonomous multi-modal mobility morphobot (M4) robot: traversability estimation and 3D path planning,,2023,dissertation,robotics,4,1.0,4.0,86.35051546391752,4,,True,True,post,86.35051546391752,landmark,True1024399978538,10.3390/diagnostics14131333,,Exploring Augmented Reality Integration in Diagnostic Imaging: Myth or Reality?,Diagnostics,2024,dissertation,hci,3,35.0,60.0,658.2127659574469,60,,True,True,post,658.2127659574469,landmark,True1034414820664,10.21203/rs.3.rs-8148142/v1,2503.17473,How AI and Human Behaviors Shape Psychosocial Effects of Extended Chatbot Use: A Longitudinal Randomized Controlled Study,Research Square,2026,preprint,hci,2,97.0,97.0,632.2017543859649,97,,False,True,post,344.8563961944324,landmark,True1042518959197,10.25335/nw4q-va87,,Cybersickness prioritization and modeling,Michigan State University Libraries,2024,dissertation,hci,2,23.0,23.0,291.2096774193549,23,,False,True,post,291.2096774193549,landmark,True1054387835442,10.1145/3586183.3606763,2304.03442,Generative Agents: Interactive Simulacra of Human Behavior,ACM Symposium on User Interface Software and Technology (UIST),2023,conference-paper,hci,6,466.0,1188.0,273.9745751869643,2048,387.1409,True,True,post,273.9745751869643,landmark,True1064286221734,10.17918/00001117,,Human-computer interaction,,2022,dissertation,hci,2,3.0,3.0,267.6,8,,True,True,baseline,267.6,landmark,True1074404112583,10.1561/1100000095,2110.05352,"All One Needs to Know about Metaverse: A Complete Survey on Technological Singularity, Virtual Ecosystem, and Research Agenda",Foundations and Trends® in Human–Computer Interaction,2024,article,hci,7,1016.0,1085.0,265.27941176470586,1082,154.4433,True,True,baseline,265.27941176470586,landmark,True1084294891500,10.1109/comst.2022.3202047,2203.02662,"A Survey on Metaverse: Fundamentals, Security, and Privacy",IEEE Communications Surveys & Tutorials,2022,article,hci,7,568.0,1062.0,261.00875052618636,1575,347.0709,True,True,baseline,261.00875052618636,landmark,True1092910064571,10.1007/978-3-319-96906-0_6,,A Practical Guide to Usability Testing,Springer eBooks,2019,book-chapter,hci,1,301.0,354.0,258.125,659,109.4298,False,False,baseline,258.125,landmark,True1104409885395,10.1145/3706598.3713778,,The Impact of Generative AI on Critical Thinking: Self-Reported Reductions in Cognitive Effort and Confidence Effects From a Survey of Knowledge Workers,CHI Conference on Human Factors in Computing Systems,2025,conference-paper,hci,3,502.0,502.0,162.5548189699133,502,530.8542,True,True,post,254.1419451473349,landmark,True1112915602850,,,"Memes, Communities and Continuous Change: Chinese Internet Vernacular Explained",Data Archiving and Networked Services (DANS),2021,dissertation,hci,1,37.0,38.0,249.76068376068378,39,,False,True,baseline,249.76068376068378,landmark,True1124285585446,10.1016/j.ijinfomgt.2022.102542,,"Metaverse beyond the hype: Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy",International Journal of Information Management,2022,article,hci,2,787.0,1634.0,245.39307870858832,2732,283.5672,True,True,baseline,245.39307870858832,landmark,True1132308543850,10.17615/cd1t-9y62,,Scene-motion- and latency-perception thresholds for head-mounted displays,Carolina Digital Repository (University of North Carolina at Chapel Hill),2019,dissertation,hci,2,25.0,26.0,195.9915254237288,30,,True,True,baseline,195.9915254237288,landmark,True1144206484811,10.1109/access.2021.3140175,,"A Metaverse: Taxonomy, Components, Applications, and Open Challenges",IEEE Access,2022,article,hci,1,791.0,1282.0,192.52994302595485,1824,197.5614,True,True,baseline,192.52994302595485,landmark,True1154404301029,10.32604/cmc.2024.055575,,The Metaverse Review: Exploring the Boundless Ream of Digital Reality,"Computers, materials & continua/Computers, materials & continua (Print)",2024,dissertation,hci,2,11.0,15.0,189.9193548387097,15,,True,True,post,189.9193548387097,landmark,True1164402988539,10.3390/info15100596,2309.10892,Artificial Intelligence-Enabled Intelligent Assistant for Personalized and Adaptive Learning in Higher Education,Information,2024,article,hci,3,379.0,597.0,179.74180185270544,597,226.9308,True,True,post,179.74180185270544,landmark,True1174404826297,10.1080/10447318.2024.2431761,2303.13534,Prompting AI Art: An Investigation into the Creative Skill of Prompt Engineering,International Journal of Human-Computer Interaction,2024,article,hci,4,134.0,237.0,169.09230306674684,237,201.8338,True,True,post,169.09230306674684,landmark,True1184402128637,10.1080/0144929x.2024.2394886,,The promise and challenges of generative AI in education,Behaviour and Information Technology,2024,article,hci,3,193.0,515.0,155.05364816439413,515,197.0815,True,True,post,155.05364816439413,landmark,True1194366548330,10.1145/3544548.3581388,,Why Johnny Can’t Prompt: How Non-AI Experts Try (and Fail) to Design LLM Prompts,CHI Conference on Human Factors in Computing Systems,2023,conference-paper,hci,4,330.0,657.0,151.5162423382454,902,170.5083,True,True,post,151.5162423382454,landmark,True1204403637324,10.1038/s41562-024-02004-5,2408.12143,Promises and challenges of generative artificial intelligence for human learning,Nature Human Behaviour,2024,article,hci,2,213.0,483.0,145.41924672505314,483,184.453,False,True,post,145.41924672505314,landmark,True1214408943584,10.3389/feduc.2025.1562391,,"Digital learning in the 21st century: trends, challenges, and innovations in technology integration",Frontiers in Education,2025,article,hci,2,274.0,274.0,237.88706394470083,274,299.3436,True,True,post,138.71492623579633,landmark,True1224392772072,10.30574/gscarr.2024.18.2.0062,,AI in education: A review of personalized learning and educational technology,GSC Advanced Research and Reviews,2024,article,hci,4,285.0,425.0,127.9568941162476,425,162.6401,True,True,post,127.9568941162476,landmark,True1234392239564,10.1080/03075079.2024.2323593,,Human-AI collaboration patterns in AI-assisted academic writing,Studies in Higher Education,2024,article,hci,3,198.0,423.0,127.35474402628878,423,161.8747,True,True,post,127.35474402628878,landmark,True1243102008235,10.17863/cam.56608,,"Addressing Injustices through MOOCs: A study among peri-urban, marginalised, South African youth",Apollo (University of Cambridge),2020,dissertation,hci,2,0.0,22.0,125.7948717948718,29,,True,True,baseline,125.7948717948718,landmark,True1254405586205,10.1016/j.caeai.2024.100348,2405.11800,Generative AI in higher education: A global perspective of institutional adoption policies and guidelines,Computers and Education Artificial Intelligence,2024,article,hci,3,149.0,415.0,124.94614366645354,415,158.8133,True,True,post,124.94614366645354,landmark,True1263207138404,10.1145/3474085.3479238,2108.08985,Metaverse for Social Good,ACM International Conference on Multimedia (ACM MM),2021,conference-paper,hci,4,174.0,468.0,123.76423282498897,832,125.6356,True,True,baseline,123.76423282498897,landmark,True1274221151978,10.1145/3528223.3530127,2201.05989,Instant neural graphics primitives with a multiresolution hash encoding,ACM Transactions on Graphics,2022,article,computer_graphics,6,704.0,2033.0,763.1175545066307,4114,1035.1766,True,True,baseline,763.1175545066307,landmark,True1281483393136,10.82444/warw.33661294,,Advanced high dynamic range imaging : theory and practice,CERN Document Server (European Organization for Nuclear Research),2026,article,computer_graphics,2,214.0,214.0,573.5984721161192,216,3.1163,True,True,post,760.8172039753458,landmark,True1292910683382,,,Real-Time Simulation and Rendering of 3D Fluids,,2018,dissertation,computer_graphics,2,26.0,26.0,526.90625,62,,False,False,baseline,526.90625,landmark,True1304385318467,10.1145/3592433,2308.04079,3D Gaussian Splatting for Real-Time Radiance Field Rendering,ACM Transactions on Graphics,2023,article,computer_graphics,2,1016.0,3644.0,395.070873513716,5862,825.8872,True,True,post,395.070873513716,landmark,True1313128261063,10.21105/joss.03021,,seaborn: statistical data visualization,The Journal of Open Source Software,2021,article,computer_graphics,3,1232.0,2401.0,279.8183504232003,7190,347.307,True,True,baseline,279.8183504232003,landmark,True1324390723081,10.1145/3807511,2401.03890,A Survey on 3D Gaussian Splatting,ACM Computing Surveys,2026,article,computer_graphics,3,67.0,67.0,121.09388038942977,67,72.919,True,True,post,238.19977881471107,landmark,True1331506965148,10.2312/14803,,Mastering the Information Age Solving Problems with Visual Analytics,University of Groningen research database (University of Groningen / Centre for Information Technology),2022,book-chapter,computer_graphics,2,513.0,517.0,220.7091108671789,552,15.8992,True,True,post,220.7091108671789,landmark,True1344231645407,10.2307/j.ctv65sx8p.8,,The Fastest Texture Mapping in Town:,University of Michigan Press eBooks,2018,book-chapter,computer_graphics,2,62.0,66.0,186.86419753086417,107,78.4444,False,False,baseline,186.86419753086417,landmark,True1354252097634,10.1007/978-3-030-63416-2_778,,Statistical Shape Analysis,Springer eBooks,2021,book-chapter,computer_graphics,2,221.0,229.0,149.26305916305915,416,20.1122,False,False,baseline,149.26305916305915,landmark,True1363016007010,10.1111/cgf.14022,2004.03805,State of the Art on Neural Rendering,Computer Graphics Forum,2020,article,computer_graphics,3,129.0,213.0,133.21172638436482,430,143.0402,True,True,baseline,133.21172638436482,landmark,True137232012359,10.1109/mm.2020.2996321,,IEEE Computer Graphics and Applications,IEEE Micro,2020,article,computer_graphics,2,181.0,195.0,121.95439739413682,397,51.1209,False,True,baseline,121.95439739413682,landmark,True1384200150166,10.1145/3503250,,NeRF,Communications of the ACM,2021,article,computer_graphics,2,145.0,900.0,113.85132582911535,6744,366.1947,True,True,baseline,113.85132582911535,landmark,True1393163641770,10.2172/1782521,,Metric Type in the Target-matrix Mesh Optimization Paradigm,,2020,report,computer_graphics,3,2.0,6.0,112.49999999999999,9,,True,True,baseline,112.49999999999999,landmark,True1404385289063,10.1145/3592426,2302.12249,MERF: Memory-Efficient Radiance Fields for Real-time View Synthesis in Unbounded Scenes,ACM Transactions on Graphics,2023,article,computer_graphics,2,114.0,172.0,102.18535708938464,199,115.9383,True,True,post,102.18535708938464,landmark,True1417151814497,10.70675/339cd785z4790z4c97zb09dze298711dbccf,,Designing tools for 3D content authoring based on 3D sketching,,2023,dissertation,computer_graphics,2,0.0,2.0,98.75,2,,True,True,post,98.75,landmark,True1424385482704,10.1145/3596711.3596766,,Photo tourism: exploring photo collections in 3D,ACM eBooks,2023,book-chapter,computer_graphics,2,184.0,195.0,93.00658376005852,222,25.2247,True,False,post,93.00658376005852,landmark,True1432478400128,10.26190/unsworks/9223,,Raster scan computer graphics and incremental computing systems,"UNSWorks (University of New South Wales, Sydney, Australia)",2022,dissertation,computer_graphics,3,1.0,1.0,89.2,3,,False,True,baseline,89.2,landmark,True1447124433690,10.1145/3789201,2406.01467,RaDe-GS: Rasterizing Depth in Gaussian Splatting,ACM Transactions on Graphics,2026,article,computer_graphics,2,25.0,25.0,45.18428372739917,25,36.4595,True,True,post,88.88051448310114,landmark,True1453144501610,,,Differentiable visual computing,DSpace@MIT (Massachusetts Institute of Technology),2019,dissertation,computer_graphics,2,2.0,4.0,88.8135593220339,3,,True,True,baseline,88.8135593220339,landmark,True1463113347918,10.1109/iccv48922.2021.01245,2012.03918,NeRD: Neural Reflectance Decomposition from Image Collections,IEEE/CVF International Conference on Computer Vision (ICCV),2021,conference-paper,computer_graphics,2,90.0,203.0,84.667495854063,438,99.214,True,True,baseline,84.667495854063,landmark,True1472986023562,10.1145/3355089.3356498,,Mitsuba 2,ACM Transactions on Graphics,2019,article,computer_graphics,7,41.0,120.0,84.28304768369252,404,109.5482,True,True,baseline,84.28304768369252,landmark,True1482978950353,10.33153/citrawira.v1i1.3299,,PERANCANGAN POP UP BOOK AND SOUND TEMBANG DOLANAN SEBAGAI MEDIA PENGENALAN ALAT MUSIK GAMELAN,Institutional Repository ISI Surakarta (Institut Seni Indonesia Surakarta),2020,dissertation,computer_graphics,1,1.0,3.0,81.7,4,,True,False,baseline,81.7,landmark,True1493115857147,10.2312/pg.20201223,,Illumination Space: A Feature Space for Radiance Maps,ResearchArchive–Te Puna Rangahau (Victoria University of Wellington),2020,dissertation,computer_graphics,1,1.0,3.0,81.7,3,,True,True,baseline,81.7,landmark,True1504402660132,10.1109/cvpr52733.2024.00813,2312.16812,Spacetime Gaussian Feature Splatting for Real-Time Dynamic View Synthesis,IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings,2024,conference-paper,computer_graphics,2,110.0,208.0,77.27933949282485,208,83.0854,True,True,post,77.27933949282485,landmark,True1512885324189,10.1145/3341156,,Neural Importance Sampling,ACM Transactions on Graphics,2019,article,computer_graphics,3,52.0,110.0,77.25946037671814,321,80.9225,True,True,baseline,77.25946037671814,landmark,True1524200503948,10.7735/ksmte.2021.30.6.513,2004.10934,Comparative Analysis of Defect Detection Using YOLO of Deep Learning,Journal of the korean society of manufacturing technology engineers,2021,article,computer_vision,3,4221.0,6845.0,406.01302821116474,8,0.9035,True,True,baseline,406.01302821116474,landmark,True1531797268635,10.57702/i9oeewf6,,Caltech-UCSD Birds-200-2011 Dataset,CaltechAUTHORS (California Institute of Technology),2024,article,computer_vision,2,3520.0,3523.0,417.3662846615462,3525,69.4971,True,True,post,417.3662846615462,landmark,True1542963163009,10.1109/cvpr.2018.00474,1801.04381,MobileNetV2: Inverted Residuals and Linear Bottlenecks,IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings,2018,conference-paper,computer_vision,3,1242.0,3514.0,430.50535987748856,27247,513.8505,True,True,baseline,430.50535987748856,landmark,True1552796347433,10.48550/arxiv.1804.02767,1804.02767,YOLOv3: An Incremental Improvement,arXiv (Cornell University),2018,preprint,computer_vision,5,1204.0,3187.0,289.4544999852894,5512,,True,True,baseline,289.4544999852894,landmark,True1562752782242,10.1109/cvpr.2018.00745,,Squeeze-and-Excitation Networks,IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings,2018,conference-paper,computer_vision,4,1289.0,3107.0,380.64318529862175,30519,447.0037,True,False,baseline,380.64318529862175,landmark,True1574320002812,10.1109/jproc.2023.3238524,1905.05055,Object Detection in 20 Years: A Survey,Proceedings of the IEEE,2023,article,computer_vision,2,2067.0,3080.0,320.84963557411936,3650,278.5821,True,True,post,320.84963557411936,landmark,True1584391109864,10.1038/s41467-024-44824-z,,Segment anything in medical images,Nature Communications,2024,article,computer_vision,3,1944.0,3007.0,356.2362810040504,2941,409.3548,True,True,post,356.2362810040504,landmark,True1593030520226,10.1007/978-3-030-58452-8_13,2005.12872,End-to-End Object Detection with Transformers,Lecture notes in computer science,2020,conference-paper,computer_vision,3,864.0,2877.0,348.31327045713607,16417,486.7957,False,True,baseline,348.31327045713607,landmark,True1602904367110,10.1109/cvpr.2019.00453,1812.04948,A Style-Based Generator Architecture for Generative Adversarial Networks,IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings,2019,conference-paper,computer_vision,4,1224.0,2727.0,332.8687255628226,11051,354.5442,True,True,baseline,332.8687255628226,landmark,True1612964309882,10.1007/978-3-030-01234-2_49,1802.02611,Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation,Lecture notes in computer science,2018,conference-paper,computer_vision,3,954.0,2432.0,297.947932618683,18734,350.6183,False,True,baseline,297.947932618683,landmark,True1623034971973,10.1109/cvpr42600.2020.01079,1911.09070,EfficientDet: Scalable and Efficient Object Detection,IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings,2020,conference-paper,computer_vision,4,1099.0,2290.0,277.2462250075918,9258,321.5836,True,True,baseline,277.2462250075918,landmark,True1634388823657,10.3390/make5040083,2304.00501,A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS,Machine Learning and Knowledge Extraction,2023,article,computer_vision,4,907.0,2277.0,237.19955201372395,3052,277.6729,True,True,post,237.19955201372395,landmark,True1642954996726,10.1186/s40537-019-0197-0,,A survey on Image Data Augmentation for Deep Learning,Journal Of Big Data,2019,article,computer_vision,3,636.0,2134.0,323.4516322601615,13005,461.5342,True,True,baseline,323.4516322601615,landmark,True1653187418919,10.1109/tpami.2022.3152247,2012.12556,A Survey on Vision Transformer,IEEE Transactions on Pattern Analysis and Machine Intelligence,2022,article,computer_vision,6,982.0,2110.0,226.46590993554688,4585,293.5626,True,True,baseline,226.46590993554688,landmark,True1662995327724,10.1109/cvpr42600.2020.00813,1912.04958,Analyzing and Improving the Image Quality of StyleGAN,IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings,2020,conference-paper,computer_vision,5,1027.0,1989.0,240.80469062886468,6002,265.6208,True,True,baseline,240.80469062886468,landmark,True1672784874046,10.1109/cvpr.2019.00482,1801.07698,ArcFace: Additive Angular Margin Loss for Deep Face Recognition,IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings,2019,conference-paper,computer_vision,3,955.0,1970.0,240.46622272048424,7537,250.3062,True,True,baseline,240.46622272048424,landmark,True1683177052299,10.1109/cvpr46437.2021.01350,2103.02907,Coordinate Attention for Efficient Mobile Network Design,IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings,2021,conference-paper,computer_vision,5,672.0,1959.0,244.60506070745512,6447,314.9922,True,True,baseline,244.60506070745512,landmark,True1694386072096,10.1109/cvpr52729.2023.02155,,DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation,IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings,2023,conference-paper,computer_vision,6,1113.0,1918.0,267.65970062911276,2234,253.5618,True,False,baseline,267.65970062911276,landmark,True1703119997354,10.1145/3505244,2101.01169,Transformers in Vision: A Survey,ACM Computing Surveys,2022,article,computer_vision,9,1080.0,1899.0,203.8193189419922,3536,224.1986,True,True,baseline,203.8193189419922,landmark,True1714386047745,10.1109/cvpr52729.2023.01157,2303.03667,"Run, Don't Walk: Chasing Higher FLOPS for Faster Neural Networks",IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings,2023,conference-paper,computer_vision,7,879.0,1888.0,263.4731568236522,2343,266.4104,True,True,post,263.4731568236522,landmark,True1724224035735,10.48550/arxiv.2204.06125,2204.06125,Hierarchical Text-Conditional Image Generation with CLIP Latents,arXiv (Cornell University),2022,preprint,computer_vision,5,904.0,1882.0,408.5254913442887,2203,,True,True,baseline,408.5254913442887,landmark,True1732963091558,10.1109/cvpr.2018.00813,1711.07971,Non-local Neural Networks,IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings,2018,conference-paper,computer_vision,4,654.0,1881.0,230.44410413476265,10954,299.8487,True,True,baseline,230.44410413476265,landmark,True1744398756379,10.1109/adics58448.2024.10533619,,YOLOv8: A Novel Object Detection Algorithm with Enhanced Performance and Robustness,,2024,conference-paper,computer_vision,2,1232.0,1876.0,393.72768235909564,1876,409.6027,True,False,post,393.72768235909564,landmark,True1753171007011,10.1109/cvpr46437.2021.01549,,Exploring Simple Siamese Representation Learning,IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings,2021,conference-paper,computer_vision,3,954.0,1869.0,296.32486388384757,3498,304.7959,True,False,baseline,296.32486388384757,landmark,True1762955058313,10.1109/cvpr.2019.00326,1809.02983,Dual Attention Network for Scene Segmentation,IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings,2019,conference-paper,computer_vision,3,709.0,1862.0,227.28330289621402,6834,261.5228,True,True,baseline,227.28330289621402,landmark,True1772736941579,10.1109/cvpr.2018.00907,1707.07012,Learning Transferable Architectures for Scalable Image Recognition,IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings,2018,conference-paper,computer_vision,4,845.0,1862.0,228.11638591117918,6249,236.7712,True,True,baseline,228.11638591117918,landmark,True1783131500599,10.1109/iccv48922.2021.00061,,Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions,IEEE/CVF International Conference on Computer Vision (ICCV),2021,conference-paper,computer_vision,4,730.0,1844.0,230.24590706715023,4926,273.2358,True,False,baseline,230.24590706715023,landmark,True1792999607073,10.1109/tpami.2021.3059968,2001.05566,Image Segmentation Using Deep Learning: A Survey,IEEE Transactions on Pattern Analysis and Machine Intelligence,2021,article,computer_vision,5,1058.0,1837.0,219.61025275964155,3634,227.7791,True,True,baseline,219.61025275964155,landmark,True1804404612908,10.1007/978-3-031-72970-6_3,,Grounding DINO: Marrying DINO with Grounded Pre-training for Open-Set Object Detection,Lecture notes in computer science,2024,conference-paper,computer_vision,4,1177.0,1834.0,384.91288350030993,1834,395.6291,False,False,post,384.91288350030993,landmark,True1812963125010,10.1109/cvpr.2018.00716,,ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices,IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings,2018,conference-paper,computer_vision,3,843.0,1788.0,219.05053598774887,9452,225.5404,True,True,baseline,219.05053598774887,landmark,True1824225672218,10.1109/cvpr52688.2022.00564,2111.09881,Restormer: Efficient Transformer for High-Resolution Image Restoration,IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern 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Clouds,ACM Transactions on Graphics,2019,article,computer_vision,4,815.0,1673.0,204.21997406985423,7010,434.8669,True,True,baseline,204.21997406985423,landmark,True1862925148167,10.1109/cvpr42600.2020.01164,1903.11027,nuScenes: A Multimodal Dataset for Autonomous Driving,IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings,2020,conference-paper,computer_vision,6,935.0,1637.0,189.37948256099557,6888,578.0759,True,True,baseline,189.37948256099557,landmark,True1874402727764,10.1109/cvpr52733.2024.02484,2310.03744,Improved Baselines with Visual Instruction Tuning,IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings,2024,conference-paper,computer_vision,8,1070.0,1637.0,343.5672793293388,1637,356.7648,True,True,post,343.5672793293388,landmark,True1882998508940,10.1609/aaai.v34i07.7000,,Random Erasing Data Augmentation,Proceedings of the AAAI Conference on Artificial Intelligence,2020,conference-paper,computer_vision,3,1114.0,1602.0,193.95128928478692,3303,157.9126,True,True,baseline,193.95128928478692,landmark,True1892962730651,10.1109/tpami.2019.2929257,1812.08008,OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields,IEEE Transactions on Pattern Analysis and Machine Intelligence,2019,article,computer_vision,1,634.0,1600.0,242.51293890171436,5491,280.2052,True,True,baseline,242.51293890171436,landmark,True1902884367402,10.1109/tnnls.2018.2876865,1807.05511,Object Detection With Deep Learning: A Review,IEEE Transactions on Neural Networks and Learning 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distilled version of BERT: smaller, faster, cheaper and lighter",arXiv (Cornell University),2019,preprint,nlp,5,502.0,1462.0,165.6218897479667,4347,,True,True,baseline,165.6218897479667,landmark,True1983212386989,10.1007/s41095-022-0271-y,2111.07624,Attention mechanisms in computer vision: A survey,Computational Visual Media,2022,article,computer_vision,4,714.0,1442.0,154.76959342514627,2500,171.4269,True,True,baseline,154.76959342514627,landmark,True1993159481202,10.1109/iccv48922.2021.00951,2104.14294,Emerging Properties in Self-Supervised Vision Transformers,IEEE/CVF International Conference on Computer Vision (ICCV),2021,conference-paper,computer_vision,5,497.0,1438.0,227.9909867656355,5690,363.2871,True,True,baseline,227.9909867656355,landmark,True2002980282514,10.48550/arxiv.1910.03771,1910.03771,HuggingFace's Transformers: State-of-the-art Natural Language Processing,arXiv (Cornell University),2019,preprint,nlp,5,692.0,1434.0,162.44992469123406,2972,,True,True,baseline,162.44992469123406,landmark,True2014287824654,10.4230/oasics.slate.2022.3,,Question Answering For Toxicological Information Extraction,DROPS (Schloss Dagstuhl – Leibniz Center for Informatics),2022,article,nlp,3,1238.0,1430.0,147.34664543494497,1520,94.4336,True,True,baseline,147.34664543494497,landmark,True2024226069476,10.1109/cvpr52688.2022.00135,2112.01527,Masked-attention Mask Transformer for Universal Image Segmentation,IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings,2022,conference-paper,computer_vision,3,497.0,1420.0,191.86247481053815,3362,243.9506,True,True,baseline,191.86247481053815,landmark,True2034210598935,10.1016/j.procs.2022.01.135,,A Review of Yolo Algorithm Developments,Procedia Computer Science,2022,conference-paper,computer_vision,2,544.0,1406.0,189.9708729462089,2733,219.8066,True,True,baseline,189.9708729462089,landmark,True2043135367836,10.48550/arxiv.2103.00020,2103.00020,Learning Transferable Visual Models From Natural Language Supervision,arXiv (Cornell University),2021,preprint,computer_vision,6,625.0,1405.0,222.66089965397924,5217,,True,True,baseline,222.66089965397924,landmark,True2053042011474,10.1109/cvprw50498.2020.00203,1911.11929,CSPNet: A New Backbone that can Enhance Learning Capability of CNN,IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW),2020,conference-paper,computer_vision,3,534.0,1403.0,169.85871339984772,4922,202.034,True,True,baseline,169.85871339984772,landmark,True2064360884927,10.1109/tpami.2023.3261988,2209.04747,Diffusion Models in Vision: A Survey,IEEE Transactions on Pattern Analysis and Machine Intelligence,2023,article,computer_vision,5,632.0,1386.0,144.38233600835372,1859,168.9313,True,True,post,144.38233600835372,landmark,True2074400881081,10.1016/j.media.2024.103280,,TransUNet: Rethinking the U-Net architecture design for medical image segmentation through the lens of transformers,Medical Image Analysis,2024,article,computer_vision,3,646.0,1365.0,161.71018409395703,1365,192.8121,True,True,post,161.71018409395703,landmark,True2083014641072,10.1109/tpami.2020.2983686,,Deep High-Resolution Representation Learning for Visual Recognition,IEEE Transactions on Pattern Analysis and Machine Intelligence,2020,article,computer_vision,4,639.0,1362.0,183.6448855499229,4785,201.7077,True,False,baseline,183.6448855499229,landmark,True209