https://github.com/opengvlab/unmasked_teacher
공식 코드Unmasked Teacher: Towards Training-Efficient Video Foundation Models
Papers with Code 아카이브
Action Classification
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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/opengvlab/videomamba
공식 코드VideoMamba: State Space Model for Efficient Video Understanding
https://github.com/orangeeji/3d-ace
공식 코드https://github.com/papermsucode/mutual-modality-learning
공식 코드https://github.com/park-jungin/dualpath
공식 코드https://github.com/piergiaj/AViD
공식 코드https://github.com/piergiaj/representation-flow-cvpr19
공식 코드https://github.com/pritamqu/XKD
공식 코드XKD: Cross-modal Knowledge Distillation with Domain Alignment for Video Representation Learning
https://github.com/psmmettes/object-priors-unseen-actions
공식 코드https://github.com/rayabhisek123/autoregad-hgformer
공식 코드Autoregressive Adaptive Hypergraph Transformer for Skeleton-based Activity Recognition
https://github.com/rs2002/csi-bert
공식 코드Finding the Missing Data: A BERT-inspired Approach Against Package Loss in Wireless Sensing
https://github.com/ruiwang2021/mvd
공식 코드https://github.com/rvl-lab-utoronto/video_similarity_search
공식 코드SLIC: Self-Supervised Learning with Iterative Clustering for Human Action Videos
https://github.com/rwightman/posenet-python
공식 코드Three-Stream 3D/1D CNN for Fine-Grained Action Classification and Segmentation in Table Tennis
https://github.com/rwightman/pytorch-image-models
공식 코드EVA: Exploring the Limits of Masked Visual Representation Learning at Scale
https://github.com/sadafgulshad1/Hierarchical-ProtoPNet
공식 코드https://github.com/sallymmx/actionclip
공식 코드https://github.com/sauradip/gap
공식 코드https://github.com/sejong-rcv/pvlr
공식 코드Probabilistic Vision-Language Representation for Weakly Supervised Temporal Action Localization
https://github.com/sengopal/cv-activity-classification
공식 코드Multi class activity classification in videos using Motion History Image generation
https://github.com/shahroudy/NTURGB-D
공식 코드NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis
https://github.com/shanakarg/stjd-spatio-temporal-joint-density-driven-learning-for-skeleton-based-action-recognition
공식 코드Spatio-Temporal Joint Density Driven Learning for Skeleton-Based Action Recognition
https://github.com/srijandas07/VPN
공식 코드VPN: Learning Video-Pose Embedding for Activities of Daily Living
https://github.com/srijandas07/vpnplusplus
공식 코드VPN++: Rethinking Video-Pose embeddings for understanding Activities of Daily Living
https://github.com/sssste/react
공식 코드https://github.com/sujoyp/wtalc-pytorch
공식 코드W-TALC: Weakly-supervised Temporal Activity Localization and Classification
https://github.com/tailin1009/dualhead-network
공식 코드Learning Multi-Granular Spatio-Temporal Graph Network for Skeleton-based Action Recognition
https://github.com/tensorflow/models
공식 코드MoViNets: Mobile Video Networks for Efficient Video Recognition
https://github.com/tensorflow/models/tree/master/official/vision/beta/projects/assemblenet
공식 코드AssembleNet: Searching for Multi-Stream Neural Connectivity in Video Architectures
https://github.com/tensorflow/tpu/tree/master/models/official/resnet/resnet_rs
공식 코드Revisiting ResNets: Improved Training and Scaling Strategies
https://github.com/uark-cviu/direcformer
공식 코드DirecFormer: A Directed Attention in Transformer Approach to Robust Action Recognition
https://github.com/ucf-crcv/tf-covr
공식 코드From Play to Replay: Composed Video Retrieval for Temporally Fine-Grained Videos
https://github.com/valterlej/objsentzsar
공식 코드Global Semantic Descriptors for Zero-Shot Action Recognition
https://github.com/vt-vl-lab/SDN
공식 코드Why Can't I Dance in the Mall? Learning to Mitigate Scene Bias in Action Recognition
https://github.com/wanglimin/ARTNet
공식 코드https://github.com/wanglimin/UntrimmedNet
공식 코드UntrimmedNets for Weakly Supervised Action Recognition and Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.