Papers with Code 아카이브
Video Understanding
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
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- 보존된 평가 기록
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A crucial task of Video Understanding is to recognise and localise (in space and time) different actions or events appearing in the video.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/812618101/TAL-Demo
Gaussian Temporal Awareness Networks for Action Localization
https://github.com/AI-secure/Transferability-Reduced-Smooth-Ensemble
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/AMAP-ML/RealQA
https://github.com/Aaron-Pu/cma_xdVioDet
https://github.com/BM-K/Question-Difficulty-Estimation
DramaQA: Character-Centered Video Story Understanding with Hierarchical QA
https://github.com/BoChenUIUC/YOWO
https://github.com/CMU-CREATE-Lab/deep-smoke-machine
https://github.com/Cadden/paddle-adver
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/ChetanTayal138/HAR-Web
TS-LSTM and Temporal-Inception: Exploiting Spatiotemporal Dynamics for Activity Recognition
https://github.com/ChienliMa/DeConvNet
https://github.com/DASH-Lab/VFP290K
https://github.com/DELTA37/TVN
https://github.com/DegardinBruno/human_self_learning_anomaly
https://github.com/DominikBatic/EndoViT
Whether and When does Endoscopy Domain Pretraining Make Sense?
https://github.com/EPFL-VILAB/XDEnsembles
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/EndyWon/Deep-Feature-Perturbation
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
https://github.com/EquipoVandV/mmactionVandV
https://github.com/Hadisalman/robust-verify-benchmark
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/Image-X-Institute/lpips_torch2tf
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
https://github.com/Jeffkang-94/pytorch-adversarial-attack
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/JerryX1110/awesome-rvos
End-to-End Referring Video Object Segmentation with Multimodal Transformers
https://github.com/Ji4chenLi/t2v-turbo
https://github.com/JonathonLuiten/HOTA-metrics
https://github.com/JonathonLuiten/TrackEval
https://github.com/June01/tcc_Temporal_Cycle_Consistency_Loss.pytorch
https://github.com/KiUngSong/Vision
https://github.com/KnowledgeDiscovery/FaceSec
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/LADI-Dataset/ladi-overview
An overview on the evaluated video retrieval tasks at TRECVID 2022
https://github.com/LONG-9621/IQA01
Image Quality Assessment: Unifying Structure and Texture Similarity
https://github.com/Lee-Ft/RHA
Relation-aware Hierarchical Attention Framework for Video Question Answering
https://github.com/LijieFan/tvnet
End-to-End Learning of Motion Representation for Video Understanding
https://github.com/LisaAnne/TemporalLanguageRelease
https://github.com/MCG-NJU/VideoChat-Online
https://github.com/MS-Mind/MS-Code-06/tree/main/nima
https://github.com/MS-Mind/MS-Code-08/tree/main/nima
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.