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,ntitle,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,batchnormalizationacceleratingdeepnetworktrainingbyreducinginternalcovariateshift,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,learningmultiplelayersoffeaturesfromtinyimages,2765.184913506997,landmark,True44386076325,10.1109/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,yolov7trainablebagoffreebiessetsnewstateoftheartforrealtimeobjectdetectors,1477.2917574868547,landmark,True53119786062,,,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,animageisworth16x16wordstransformersforimagerecognitionatscale,1181.820775699879,landmark,True63138516171,10.1109/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,swintransformerhierarchicalvisiontransformerusingshiftedwindows,1172.5809724878566,landmark,True74402754006,10.1109/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,detrsbeatyolosonrealtimeobjectdetection,1001.7389274520061,landmark,True84403770406,10.1007/978-3-031-72751-1_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,yolov9learningwhatyouwanttolearnusingprogrammablegradientinformation,941.504468583637,landmark,True94312933868,10.1109/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,highresolutionimagesynthesiswithlatentdiffusionmodels,907.833780459159,landmark,True104226002507,10.1109/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,maskedautoencodersarescalablevisionlearners,849.9419256281733,landmark,True114399300671,10.1002/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,theeffectsofbackprojectionvariantsinbpfliketofpetreconstructionusingcnnfiltrationbasedonsimulatedandclinicalbraindata,822.2465127032651,landmark,True127106270182,10.82419/203,,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,bridgingthegapbetweenobjectandimagelevelrepresentationsforopenvocabularydetection,720.1328671328672,landmark,True134415795831,10.52202/079017-3429,2405.14458,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,yolov10realtimeendtoendobjectdetection,585.1347432927286,landmark,True143035524453,10.1109/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,momentumcontrastforunsupervisedvisualrepresentationlearning,538.8557516528527,landmark,True154321232185,10.1007/978-3-031-25066-8_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,swinunetunetlikepuretransformerformedicalimagesegmentation,536.0171585591356,landmark,True163005680577,10.48550/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,asimpleframeworkforcontrastivelearningofvisualrepresentations,523.1590380139643,landmark,True173182555022,,,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,generativeadversarialnetworksforsemanticimagesynthesisandunconditionalsynthesiswithlimiteddata,515.6,landmark,True183152382703,,,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,evolutionofyoloalgorithmandyolov5thestateoftheartobjectdetectionalgorithm,513.8375350140057,landmark,True194312443924,10.1109/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,aconvnetforthe2020s,496.0050317109053,landmark,True202335728318,10.57702/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,readingdigitsinnaturalimageswithunsupervisedfeaturelearning,491.5278782460276,landmark,True214390873054,10.1109/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,addingconditionalcontroltotexttoimagediffusionmodels,471.1257295744967,landmark,True222963163009,10.1109/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,mobilenetv2invertedresidualsandlinearbottlenecks,430.50535987748856,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,uprithviintegratingafoundationmodelandunetforenhancedfloodinundationmapping,426.921221602615,landmark,True244224035735,10.48550/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,hierarchicaltextconditionalimagegenerationwithcliplatents,408.5254913442887,landmark,True254200503948,10.7735/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,comparativeanalysisofdefectdetectionusingyoloofdeeplearning,406.01302821116474,landmark,True264398756379,10.1109/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,yolov8anovelobjectdetectionalgorithmwithenhancedperformanceandrobustness,393.72768235909564,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,attentionisallyouneed,10699.68580569256,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,bertpretrainingofdeepbidirectionaltransformersforlanguageunderstanding,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,traductionnonsupervisedelangagesdeprogrammation,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,aiassistedpipelinefordynamicgenerationoftrustworthyhealthsupplementcontentatscale,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,deepcontextualizedwordrepresentations,741.8627823725466,landmark,True324224308101,10.48550/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,palmscalinglanguagemodelingwithpathways,481.54051809067556,landmark,True334411403346,10.1145/3744746,2307.06435,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,acomprehensiveoverviewoflargelanguagemodels,479.357809721667,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,neuraltransferlearningfornaturallanguageprocessing,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,57thewordnetlexicaldatabaseofenglish,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,bartdenoisingsequencetosequencepretrainingfornaturallanguagegenerationtranslationandcomprehension,413.1416402072767,landmark,True374391136507,10.1145/3641289,2307.03109,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,asurveyonevaluationoflargelanguagemodels,373.1207027772732,landmark,True382979826702,10.18653/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,transformersstateoftheartnaturallanguageprocessing,369.2054794991272,landmark,True394404534210,10.1145/3703155,2311.05232,"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,asurveyonhallucinationinlargelanguagemodelsprinciplestaxonomychallengesandopenquestions,295.4907296628272,landmark,True407125799258,10.1109/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,parameterefficientfinetuningmethodsforpretrainedlanguagemodelsacriticalreviewandassessment,291.5790974019528,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,unsupervisedcrosslingualrepresentationlearningatscale,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,energyandpolicyconsiderationsfordeeplearninginnlp,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,leveragingorthographicinformationtoimprovemachinetranslationofunderresourcedlanguages,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,asurveyonincontextlearning,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,bertopicneuraltopicmodelingwithaclassbasedtfidfprocedure,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,mt5amassivelymultilingualpretrainedtexttotexttransformer,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,historiaehistoryofsocioculturaltransformationaslinguisticdatascienceahumanitiesusecase,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,analysingofftheshelfoptionsforquestionansweringwithportuguesefaqs,215.05128749975853,landmark,True492798812533,10.18653/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,universallanguagemodelfinetuningfortextclassification,208.11178866806569,landmark,True503156636935,10.18653/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,simcsesimplecontrastivelearningofsentenceembeddings,204.20889496115336,landmark,True514402670856,10.18653/v1/2024.findings-acl.137,2402.03216,"M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation",,2024,conference-paper,nlp,6,315.0,700.0,194.5733507917222,700,212.9491,True,True,post,m3embeddingmultilingualitymultifunctionalitymultigranularitytextembeddingsthroughselfknowledgedistillation,194.5733507917222,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,perceptualsignalanalysisandeigenvalueweightedmetricsformusicalaudioqualityassessment,2139.9370278891147,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,kaldispeechrecognitiontoolkit,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,areviewofmachinelearningapproachesforthepersonalizationofamplificationinhearingaids,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,robustspeechrecognitionvialargescaleweaksupervision,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,specaugmentasimpledataaugmentationmethodforautomaticspeechrecognition,160.2755403270008,landmark,True572952218014,10.1109/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,convtasnetsurpassingidealtimefrequencymagnitudemaskingforspeechseparation,126.90333200584095,landmark,True584406417959,10.1109/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,neuralcodeclanguagemodelsarezeroshottexttospeechsynthesizers,122.78901051211342,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,xvectorsrobustdnnembeddingsforspeakerrecognition,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,conformerconvolutionaugmentedtransformerforspeechrecognition,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,supervisedspeechseparationbasedondeeplearninganoverview,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,aprobabilisticmodelofhierarchicalmusicanalysis,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,naturalttssynthesisbyconditioningwavenetonmelspectrogrampredictions,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,speechbrainageneralpurposespeechtoolkit,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,hubertselfsupervisedspeechrepresentationlearningbymaskedpredictionofhiddenunits,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,modelingmusicstudiesofmusictranscriptionmusicperceptionandmusicproduction,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,largescalecontrastivelanguageaudiopretrainingwithfeaturefusionandkeywordtocaptionaugmentation,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,claplearningaudioconceptsfromnaturallanguagesupervision,86.6908480012218,landmark,True693209984917,10.1109/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,wavlmlargescaleselfsupervisedpretrainingforfullstackspeechprocessing,82.43168975381538,landmark,True703196974791,10.21437/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,astaudiospectrogramtransformer,72.25665452507705,landmark,True712775304348,10.1109/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,stateoftheartspeechrecognitionwithsequencetosequencemodels,70.45451178866807,landmark,True723015199127,10.1109/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,dualpathrnnefficientlongsequencemodelingfortimedomainsinglechannelspeechseparation,68.6972461420411,landmark,True732808631503,10.21437/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,voxceleb2deepspeakerrecognition,68.44152573756327,landmark,True742972333964,10.21437/interspeech.2019-2008,,GELP: GAN-Excited Liner Prediction for Speech Synthesis from Mel-Spectrogram,Aaltodoc (Aalto University),2019,dissertation,speech_audio,3,8.0,17.0,61.90681003584229,34,,True,True,baseline,gelpganexcitedlinerpredictionforspeechsynthesisfrommelspectrogram,61.90681003584229,landmark,True754221145109,10.48550/arxiv.2202.03555,2202.03555,"data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language",arXiv (Cornell University),2022,preprint,speech_audio,8,152.0,208.0,60.446848378310506,231,,True,True,baseline,data2vecageneralframeworkforselfsupervisedlearninginspeechvisionandlanguage,60.446848378310506,landmark,True762898847420,10.1109/icassp.2019.8683143,1811.00002,Waveglow: A Flow-based Generative Network for Speech Synthesis,IEEE International Conference on Acoustics Speech and Signal Processing,2019,conference-paper,speech_audio,4,233.0,453.0,60.25296246317956,972,60.7013,True,True,baseline,waveglowaflowbasedgenerativenetworkforspeechsynthesis,60.25296246317956,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,relationshipbetweeneducationalroboticsandexecutivefunctionsinchildrenwithneurodevelopmentaldisorders,296.6500382263346,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,rt2visionlanguageactionmodelstransferwebknowledgetoroboticcontrol,151.2,landmark,True794293774946,10.1007/978-981-19-2980-9_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,employingadaptivelearningandintelligenttutoringrobotsforvirtualclassroomsandsmartcampusesreformingeducationintheageofartificialintelligence,135.97268806899856,landmark,True802963188159,10.15607/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,posecnnaconvolutionalneuralnetworkfor6dobjectposeestimationinclutteredscenes,129.2718583898181,landmark,True814383097638,10.1109/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,codeaspolicieslanguagemodelprogramsforembodiedcontrol,125.95880564003319,landmark,True824385430679,10.15607/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,rt1roboticstransformerforrealworldcontrolatscale,120.28942691259529,landmark,True834414079054,10.15607/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,avisionlanguageactionflowmodelforgeneralrobotcontrol,119.73020953850207,landmark,True842990747716,10.1177/0278364919887447,,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,learningdexterousinhandmanipulation,118.1470865906983,landmark,True854383108457,10.1109/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,progpromptgeneratingsituatedrobottaskplansusinglargelanguagemodels,113.46421800672258,landmark,True861581527321,10.7907/2e18-0e29,,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,collisiondetectionandavoidanceincomputercontrolledmanipulators,110.6320610687023,landmark,True873043971245,10.1109/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,orbslam3anaccurateopensourcelibraryforvisualvisualinertialandmultimapslam,104.21312513733956,landmark,True887126053009,10.1016/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,informationtheoreticgraphfusionwithvisionlanguageactionmodelforpolicyreasoninganddualroboticcontrol,103.95428689982666,landmark,True894323572061,10.48550/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,palmeanembodiedmultimodallanguagemodel,103.01438010104935,landmark,True903003533476,10.1109/tits.2021.3054625,2002.00444,Deep 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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,asurveyonvisionlanguageactionmodelsforembodiedai,91.27693483887218,landmark,True944385403811,10.15607/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,diffusionpolicyvisuomotorpolicylearningviaactiondiffusion,89.24929343211716,landmark,True952925329277,10.23919/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,controlbarrierfunctionstheoryandapplications,87.20226791818132,landmark,True964388761637,10.17760/d20592696,2308.13972,Towards autonomous multi-modal mobility morphobot (M4) robot: traversability estimation and 3D path 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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,generativeagentsinteractivesimulacraofhumanbehavior,273.9745751869643,landmark,True1054404112583,10.1561/1100000095,2110.05352,"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,alloneneedstoknowaboutmetaverseacompletesurveyontechnologicalsingularityvirtualecosystemandresearchagenda,265.27941176470586,landmark,True1064294891500,10.1109/comst.2022.3202047,2203.02662,"A Survey on Metaverse: Fundamentals, Security, and Privacy",IEEE Communications Surveys & 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Behaviour,2024,article,hci,2,213.0,483.0,145.41924672505314,483,184.453,False,True,post,promisesandchallengesofgenerativeartificialintelligenceforhumanlearning,145.41924672505314,landmark,True1204392772072,10.30574/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,aiineducationareviewofpersonalizedlearningandeducationaltechnology,127.9568941162476,landmark,True1214392239564,10.1080/03075079.2024.2323593,,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,humanaicollaborationpatternsinaiassistedacademicwriting,127.35474402628878,landmark,True1223102008235,10.17863/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,addressinginjusticesthroughmoocsastudyamongperiurbanmarginalisedsouthafricanyouth,125.7948717948718,landmark,True1234405586205,10.1016/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,generativeaiinhighereducationaglobalperspectiveofinstitutionaladoptionpoliciesandguidelines,124.94614366645354,landmark,True1243207138404,10.1145/3474085.3479238,2108.08985,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,metaverseforsocialgood,123.76423282498897,landmark,True1254408943584,10.3389/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,digitallearninginthe21stcenturytrendschallengesandinnovationsintechnologyintegration,119.73020953850207,landmark,True1264377235537,10.1109/jiot.2023.3278329,,"A Survey on the Metaverse: The State-of-the-Art, Technologies, Applications, and Challenges",IEEE Internet of Things Journal,2023,article,hci,1,393.0,633.0,119.44364807995791,716,119.1015,True,False,post,asurveyonthemetaversethestateofthearttechnologiesapplicationsandchallenges,119.44364807995791,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,instantneuralgraphicsprimitiveswithamultiresolutionhashencoding,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,advancedhighdynamicrangeimagingtheoryandpractice,542.5906682088513,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,realtimesimulationandrenderingof3dfluids,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,3dgaussiansplattingforrealtimeradiancefieldrendering,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,seabornstatisticaldatavisualization,279.8183504232003,landmark,True1321506965148,10.2312/14803,,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,masteringtheinformationagesolvingproblemswithvisualanalytics,220.7091108671789,landmark,True1334231645407,10.2307/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,thefastesttexturemappingintown,186.86419753086417,landmark,True1344390723081,10.1145/3807511,2401.03890,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,asurveyon3dgaussiansplatting,169.8765176167899,landmark,True1353016007010,10.1111/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,stateoftheartonneuralrendering,133.21172638436482,landmark,True1363163641770,10.2172/1782521,,Metric Type in the Target-matrix Mesh Optimization Paradigm,,2020,report,computer_graphics,3,2.0,6.0,112.49999999999999,9,,True,True,baseline,metrictypeinthetargetmatrixmeshoptimizationparadigm,112.49999999999999,landmark,True1374385289063,10.1145/3592426,2302.12249,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,merfmemoryefficientradiancefieldsforrealtimeviewsynthesisinunboundedscenes,102.18535708938464,landmark,True1387151814497,10.70675/339cd785z4790z4c97zb09dze298711dbccf,,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,designingtoolsfor3dcontentauthoringbasedon3dsketching,98.75,landmark,True1394385482704,10.1145/3596711.3596766,,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,phototourismexploringphotocollectionsin3d,93.00658376005852,landmark,True1402478400128,10.26190/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,rasterscancomputergraphicsandincrementalcomputingsystems,89.2,landmark,True1413113347918,10.1109/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,nerdneuralreflectancedecompositionfromimagecollections,84.667495854063,landmark,True1422978950353,10.33153/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,perancanganpopupbookandsoundtembangdolanansebagaimediapengenalanalatmusikgamelan,81.7,landmark,True1434402660132,10.1109/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,spacetimegaussianfeaturesplattingforrealtimedynamicviewsynthesis,77.27933949282485,landmark,True1442902812770,10.1145/3272127.3275109,,Differentiable Monte Carlo ray tracing through edge sampling,ACM Transactions on Graphics,2018,article,computer_graphics,5,38.0,105.0,75.6115107913669,428,97.9479,True,True,baseline,differentiablemontecarloraytracingthroughedgesampling,75.6115107913669,landmark,True1454396833408,10.1007/s10660-024-09855-0,,Integration of artificial intelligence technology and visual communication design in metaverse e-commerce and its potential opportunities,Electronic Commerce Research,2024,book-chapter,computer_graphics,2,10.0,17.0,73.82191780821917,17,84.1119,False,False,post,integrationofartificialintelligencetechnologyandvisualcommunicationdesigninmetaverseecommerceanditspotentialopportunities,73.82191780821917,landmark,True1462942551262,10.1145/3306346.3323008,1905.00824,Single image portrait relighting,ACM Transactions on Graphics,2019,article,computer_graphics,2,41.0,103.0,72.34294926183607,259,80.372,True,True,baseline,singleimageportraitrelighting,72.34294926183607,landmark,True1473135538239,10.1007/978-3-030-92540-6_7,2103.04183,HexDom: Polycube-Based Hexahedral-Dominant Mesh Generation,SEMA SIMAI Springer series,2022,book-chapter,computer_graphics,3,17.0,21.0,69.5427135678392,25,60.724,False,True,baseline,hexdompolycubebasedhexahedraldominantmeshgeneration,69.5427135678392,landmark,True1482972175553,10.1029/2019gc008515,,The Generic Mapping Tools Version 6,Geochemistry Geophysics Geosystems,2019,article,computer_graphics,3,99.0,447.0,67.9687872664635,3192,104.5271,True,True,baseline,thegenericmappingtoolsversion6,67.9687872664635,landmark,True1492202024431,10.1201/9781003059325-5,,Signed Distance Fields for Polygon Soup Meshes,A K Peters/CRC Press eBooks,2020,book-chapter,computer_graphics,3,34.0,35.0,67.15547703180212,39,32.903,True,False,baseline,signeddistancefieldsforpolygonsoupmeshes,67.15547703180212,landmark,True1502521773018,10.17615/3myy-vc96,,Efficient computation of discrete Voronoi diagram and homotopy-preserving simplified medial axis of a 3d polyhedron,Carolina Digital Repository (University of North Carolina at Chapel Hill),2019,dissertation,computer_graphics,4,3.0,3.0,66.61016949152543,3,,True,True,baseline,efficientcomputationofdiscretevoronoidiagramandhomotopypreservingsimplifiedmedialaxisofa3dpolyhedron,66.61016949152543,landmark,True1513045012476,10.70675/aa144e03zf76fz490bzab50ze72464fe07f4,,Kinetic data structures for the geometric modeling of urban environments,,2019,dissertation,computer_graphics,3,2.0,3.0,66.61016949152543,3,,True,True,baseline,kineticdatastructuresforthegeometricmodelingofurbanenvironments,66.61016949152543,landmark,True1522796347433,10.48550/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,yolov3anincrementalimprovement,289.4544999852894,landmark,True1534320002812,10.1109/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,objectdetectionin20yearsasurvey,320.84963557411936,landmark,True1544391109864,10.1038/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,segmentanythinginmedicalimages,356.2362810040504,landmark,True1553030520226,10.1007/978-3-030-58452-8_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,endtoendobjectdetectionwithtransformers,348.31327045713607,landmark,True1562904367110,10.1109/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,astylebasedgeneratorarchitectureforgenerativeadversarialnetworks,332.8687255628226,landmark,True1572964309882,10.1007/978-3-030-01234-2_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,encoderdecoderwithatrousseparableconvolutionforsemanticimagesegmentation,297.947932618683,landmark,True1583034971973,10.1109/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,efficientdetscalableandefficientobjectdetection,277.2462250075918,landmark,True1594388823657,10.3390/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,acomprehensivereviewofyoloarchitecturesincomputervisionfromyolov1toyolov8andyolonas,237.19955201372395,landmark,True1602954996726,10.1186/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,asurveyonimagedataaugmentationfordeeplearning,323.4516322601615,landmark,True1613187418919,10.1109/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,asurveyonvisiontransformer,226.46590993554688,landmark,True1622995327724,10.1109/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,analyzingandimprovingtheimagequalityofstylegan,240.80469062886468,landmark,True1632784874046,10.1109/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,arcfaceadditiveangularmarginlossfordeepfacerecognition,240.46622272048424,landmark,True1643177052299,10.1109/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,coordinateattentionforefficientmobilenetworkdesign,244.60506070745512,landmark,True1654386072096,10.1109/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,dreamboothfinetuningtexttoimagediffusionmodelsforsubjectdrivengeneration,267.65970062911276,landmark,True1663119997354,10.1145/3505244,2101.01169,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,transformersinvisionasurvey,203.8193189419922,landmark,True1674386047745,10.1109/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,rundontwalkchasinghigherflopsforfasterneuralnetworks,263.4731568236522,landmark,True1683171007011,10.1109/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,exploringsimplesiameserepresentationlearning,296.32486388384757,landmark,True1692955058313,10.1109/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,dualattentionnetworkforscenesegmentation,227.28330289621402,landmark,True1702736941579,10.1109/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,learningtransferablearchitecturesforscalableimagerecognition,228.11638591117918,landmark,True1713131500599,10.1109/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,pyramidvisiontransformeraversatilebackbonefordensepredictionwithoutconvolutions,230.24590706715023,landmark,True1722999607073,10.1109/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,imagesegmentationusingdeeplearningasurvey,219.61025275964155,landmark,True1734404612908,10.1007/978-3-031-72970-6_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,groundingdinomarryingdinowithgroundedpretrainingforopensetobjectdetection,384.91288350030993,landmark,True1742963125010,10.1109/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,shufflenetanextremelyefficientconvolutionalneuralnetworkformobiledevices,219.05053598774887,landmark,True1754225672218,10.1109/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 Recognition/Proceedings,2022,conference-paper,computer_vision,6,674.0,1779.0,240.36855118869533,4143,303.731,True,True,baseline,restormerefficienttransformerforhighresolutionimagerestoration,240.36855118869533,landmark,True1763170841864,10.1109/cvpr46437.2021.00681,2012.15840,Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers,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,4,814.0,1733.0,216.38620224911676,3695,231.1329,True,True,baseline,rethinkingsemanticsegmentationfromasequencetosequenceperspectivewithtransformers,216.38620224911676,landmark,True1773017855299,10.1016/j.compbiomed.2020.103792,,Automated detection of COVID-19 cases using deep neural networks with X-ray images,Computers in Biology and Medicine,2020,article,computer_vision,2,1049.0,1675.0,53.07790388996886,2594,236.7839,False,True,baseline,automateddetectionofcovid19casesusingdeepneuralnetworkswithxrayimages,53.07790388996886,landmark,True1782979750740,10.1145/3326362,1801.07829,Dynamic Graph CNN for Learning on Point Clouds,ACM Transactions on Graphics,2019,article,computer_vision,4,815.0,1673.0,204.21997406985423,7010,434.8669,True,True,baseline,dynamicgraphcnnforlearningonpointclouds,204.21997406985423,landmark,True1792925148167,10.1109/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 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Intelligence,2020,conference-paper,computer_vision,3,1114.0,1602.0,193.95128928478692,3303,157.9126,True,True,baseline,randomerasingdataaugmentation,193.95128928478692,landmark,True1822962730651,10.1109/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,openposerealtimemultiperson2dposeestimationusingpartaffinityfields,242.51293890171436,landmark,True1832884367402,10.1109/tnnls.2018.2876865,1807.05511,Object Detection With Deep Learning: A Review,IEEE Transactions on Neural Networks and Learning Systems,2019,article,computer_vision,2,699.0,1580.0,239.4815271654429,5487,286.797,True,True,baseline,objectdetectionwithdeeplearningareview,239.4815271654429,landmark,True1843013601031,10.1007/s13246-020-00865-4,2003.11617,Covid-19: automatic detection from X-ray images utilizing 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