https://github.com/isee-laboratory/egoexo-fitness
공식 코드EgoExo-Fitness: Towards Egocentric and Exocentric Full-Body Action Understanding
Papers with Code 아카이브
Action Classification
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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Image source: The Kinetics Human Action Video Dataset
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/jeffhernandez1995/vic-mae
공식 코드https://github.com/jyrao/UniSoccer
공식 코드https://github.com/kahnchana/svt
공식 코드https://github.com/kpeng9510/proformer
공식 코드Delving Deep into One-Shot Skeleton-based Action Recognition with Diverse Occlusions
https://github.com/kpeng9510/trans4soar
공식 코드Delving Deep into One-Shot Skeleton-based Action Recognition with Diverse Occlusions
https://github.com/kreimanlab/DeepLearning-vs-HighLevelVision
공식 코드https://github.com/leexinhao/ZeroI2V
공식 코드ZeroI2V: Zero-Cost Adaptation of Pre-trained Transformers from Image to Video
https://github.com/linziyi96/st-adapter
공식 코드ST-Adapter: Parameter-Efficient Image-to-Video Transfer Learning
https://github.com/liqiang0307/cltdr-gmg
공식 코드Temporal Action Localization with Cross Layer Task Decoupling and Refinement
https://github.com/makecent/apn
공식 코드https://github.com/martinxm/tps
공식 코드Spatiotemporal Self-attention Modeling with Temporal Patch Shift for Action Recognition
https://github.com/mcg-nju/zeroi2v
공식 코드ZeroI2V: Zero-Cost Adaptation of Pre-trained Transformers from Image to Video
https://github.com/mchengny/RWF2000-Video-Database-for-Violence-Detection
공식 코드RWF-2000: An Open Large Scale Video Database for Violence Detection
https://github.com/mgiant/2p-gcn
공식 코드Two-person Graph Convolutional Network for Skeleton-based Human Interaction Recognition
https://github.com/microsoft/CtP
공식 코드Unsupervised Visual Representation Learning by Tracking Patches in Video
https://github.com/microsoft/Swin-Transformer
공식 코드https://github.com/microsoft/VideoX/tree/master/X-CLIP
공식 코드Expanding Language-Image Pretrained Models for General Video Recognition
https://github.com/microsoft/videox
공식 코드Expanding Language-Image Pretrained Models for General Video Recognition
https://github.com/minghchen/carl_code
공식 코드Frame-wise Action Representations for Long Videos via Sequence Contrastive Learning
https://github.com/mohitsharma29/NoisyActions2M
공식 코드NoisyActions2M: A Multimedia Dataset for Video Understanding from Noisy Labels
https://github.com/mondalanindya/msqnet
공식 코드Actor-agnostic Multi-label Action Recognition with Multi-modal Query
https://github.com/moohnai/mofo
공식 코드MOFO: MOtion FOcused Self-Supervision for Video Understanding
https://github.com/musicalOffering/sola
공식 코드https://github.com/mzolfaghari/ECO-efficient-video-understanding
공식 코드ECO: Efficient Convolutional Network for Online Video Understanding
https://github.com/naraysa/3c-net
공식 코드3C-Net: Category Count and Center Loss for Weakly-Supervised Action Localization
https://github.com/ncgarcia/modality-distillation
공식 코드Modality Distillation with Multiple Stream Networks for Action Recognition
https://github.com/newsdata/SoccerDB
공식 코드SoccerDB: A Large-Scale Database for Comprehensive Video Understanding
https://github.com/noureldien/timeception
공식 코드https://github.com/okankop/Dissected-3D-CNNs
공식 코드Dissected 3D CNNs: Temporal Skip Connections for Efficient Online Video Processing
https://github.com/okankop/MFF-pytorch
공식 코드Motion Fused Frames: Data Level Fusion Strategy for Hand Gesture Recognition
https://github.com/open-mmlab/mmaction
공식 코드Omni-sourced Webly-supervised Learning for Video Recognition
https://github.com/opengvlab/efficient-video-recognition
공식 코드https://github.com/opengvlab/fluxvit
공식 코드Make Your Training Flexible: Towards Deployment-Efficient Video Models
https://github.com/opengvlab/internvideo
공식 코드InternVideo: General Video Foundation Models via Generative and Discriminative Learning
https://github.com/opengvlab/internvideo2
공식 코드InternVideo2: Scaling Foundation Models for Multimodal Video Understanding
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.