https://github.com/pvtokmakov/video_cluster
공식 코드Unsupervised Learning of Video Representations via Dense Trajectory Clustering
Papers with Code 아카이브
Action Recognition In Videos
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Action Recognition in Videos is a task in computer vision and pattern recognition where the goal is to identify and categorize human actions performed in a video sequence. The task involves analyzing the spatiotemporal dynamics of the actions and mapping them to a predefined set of action classes, such as running, jumping, or swimming.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/quiil/quiil_thompson_solution
공식 코드https://github.com/rana2149/actnetformer
공식 코드ActNetFormer: Transformer-ResNet Hybrid Method for Semi-Supervised Action Recognition in Videos
https://github.com/sallymmx/actionclip
공식 코드https://github.com/sanketsans/ganov2
공식 코드Enhancing Next Active Object-based Egocentric Action Anticipation with Guided Attention
https://github.com/srijandas07/clip_baseline_lta_ego4d
공식 코드Video + CLIP Baseline for Ego4D Long-term Action Anticipation
https://github.com/steveliao93/gcn_logsigrnn
공식 코드Logsig-RNN: a novel network for robust and efficient skeleton-based action recognition
https://github.com/stoneMo/YOWOv2
공식 코드Towards Improving Spatiotemporal Action Recognition in Videos
https://github.com/stonybrooknlp/action-anticipation-lmtovideo
공식 코드https://github.com/tonysy/PyAction
공식 코드Learning Implicit Temporal Alignment for Few-shot Video Classification
https://github.com/uark-cviu/direcformer
공식 코드DirecFormer: A Directed Attention in Transformer Approach to Robust Action Recognition
https://github.com/vita-epfl/bounding-box-prediction
공식 코드https://github.com/wei-tim/YOWO
공식 코드You Only Watch Once: A Unified CNN Architecture for Real-Time Spatiotemporal Action Localization
https://github.com/xingyul/cpnet
공식 코드Learning Video Representations from Correspondence Proposals
https://github.com/yjxiong/caffe
공식 코드https://github.com/yjxiong/temporal-segment-networks
공식 코드Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
https://github.com/zch-yu/epic-affordance-annotation
공식 코드Fine-grained Affordance Annotation for Egocentric Hand-Object Interaction Videos
https://github.com/zeyun-zhong/afft
공식 코드Anticipative Feature Fusion Transformer for Multi-Modal Action Anticipation
https://github.com/zhanghaotong1/wslvideodenseanticipation
공식 코드https://github.com/zhaoyue-zephyrus/testra
공식 코드Real-time Online Video Detection with Temporal Smoothing Transformers
https://github.com/2023-MindSpore-1/ms-code-6/tree/main/C3D
Learning Spatiotemporal Features with 3D Convolutional Networks
https://github.com/2023-MindSpore-1/ms-code-7/tree/main/trn
https://github.com/2023-MindSpore-1/ms-code-7/tree/main/tsn
https://github.com/2023-MindSpore-4/Code7/tree/main/slowfast
https://github.com/2023-MindSpore-4/Code8/tree/main/C3D
R-C3D: Region Convolutional 3D Network for Temporal Activity Detection
https://github.com/2024-MindSpore-1/Code5/tree/main/3dcnn
Learning Spatiotemporal Features with 3D Convolutional Networks
https://github.com/2024-MindSpore-1/Code6/tree/main/3dcnn
Learning Spatiotemporal Features with 3D Convolutional Networks
https://github.com/2024-MindSpore-1/Code6/tree/main/C3D
Learning Spatiotemporal Features with 3D Convolutional Networks
https://github.com/AKASH2907/Content-based-Video-Recommendation
Learning Spatiotemporal Features with 3D Convolutional Networks
https://github.com/AKASH2907/Content-based-Video-Relevance-Prediction
Learning Spatiotemporal Features with 3D Convolutional Networks
https://github.com/AbdalaDiasse/Video-classification-for-oil-quality-estimation
https://github.com/Alexyuda/action_recognition
UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild
https://github.com/BestJuly/IIC
Self-supervised Video Representation Learning Using Inter-intra Contrastive Framework
https://github.com/BoChenUIUC/YOWO
You Only Watch Once: A Unified CNN Architecture for Real-Time Spatiotemporal Action Localization
https://github.com/Cloud-Computing-IoT/Cloud-Enabled-Smart-Speaker
https://github.com/Cloud-Computing-IoT/speakEasy
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.