https://github.com/christianll9/deepstay
공식 코드DeepStay: Stay Region Extraction from Location Trajectories using Weak Supervision
Papers with Code 아카이브
Action Recognition
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 15
- 평가표
- 56
- 보존된 평가 기록
- 650
Action Recognition is a computer vision task that involves recognizing human actions in videos or images. The goal is to classify and categorize the actions being performed in the video or image into a predefined set of action classes.
In the video domain, it is an open question whether training an action classification network on a sufficiently large dataset, will give a similar boost in performance when applied to a different temporal task or dataset. The challenges of building video datasets has meant that most popular benchmarks for action recognition are small, having on the order of 10k videos.
Please note some benchmarks may be located in the Action Classification or Video Classification tasks, e.g. Kinetics-400.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/chuong/cattle_identification_action_recognition
공식 코드https://github.com/cltl/pepper
공식 코드Leolani: a reference machine with a theory of mind for social communication
https://github.com/cofly2014/tsa-mlt
공식 코드Task-Specific Alignment and Multiple Level Transformer for Few-Shot Action Recognition
https://github.com/crashbugger/mindbridge
공식 코드MindBridge: Scalable and Cross-Model Knowledge Editing via Memory-Augmented Modality
https://github.com/cseeyangchen/c2vl
공식 코드https://github.com/csiro-robotics/TCE
공식 코드Temporally Coherent Embeddings for Self-Supervised Video Representation Learning
https://github.com/cvgmi/nestedgrassmann
공식 코드Nested Grassmannians for Dimensionality Reduction with Applications
https://github.com/cvlab-columbia/hyperfuture
공식 코드https://github.com/cybercore-co-ltd/VidConv
공식 코드VidConv: A modernized 2D ConvNet for Efficient Video Recognition
https://github.com/cyfml/opstl
공식 코드Exploring Self-supervised Skeleton-based Action Recognition in Occluded Environments
https://github.com/czh-98/STAR
공식 코드STAR: Skeleton-aware Text-based 4D Avatar Generation with In-Network Motion Retargeting
https://github.com/czhaneva/mst-gcn
공식 코드Multi-Scale Spatial Temporal Graph Convolutional Network for Skeleton-Based Action Recognition
https://github.com/czhaneva/skelemixclr
공식 코드https://github.com/damnull/lagcn
공식 코드Language Knowledge-Assisted Representation Learning for Skeleton-Based Action Recognition
https://github.com/dandoge/palm
공식 코드https://github.com/danideniz/davishanddataset-events
공식 코드Event-based Vision for Early Prediction of Manipulation Actions
https://github.com/daspraty/stg-gradcam
공식 코드Towards a geometric understanding of Spatio Temporal Graph Convolution Networks
https://github.com/datamllab/autovideo
공식 코드https://github.com/daveishan/spact
공식 코드SPAct: Self-supervised Privacy Preservation for Action Recognition
https://github.com/daveishan/timebalance
공식 코드https://github.com/davelailai/sparse-st-gcn
공식 코드Are Spatial-Temporal Graph Convolution Networks for Human Action Recognition Over-Parameterized?
https://github.com/davidsvy/fractal_video
공식 코드Pre-training for Action Recognition with Automatically Generated Fractal Datasets
https://github.com/ddursun/violence-detection
공식 코드Comparative Analysis: Violence Recognition from Videos using Transfer Learning
https://github.com/decisionforce/TPN
공식 코드https://github.com/decisionforce/VTHCL
공식 코드https://github.com/deepinmotion/autogcn
공식 코드AutoGCN -- Towards Generic Human Activity Recognition with Neural Architecture Search
https://github.com/deepinmotion/shapgcn
공식 코드https://github.com/deepintostreams/gcn-devlstm
공식 코드GCN-DevLSTM: Path Development for Skeleton-Based Action Recognition
https://github.com/deepmind/brave
공식 코드https://github.com/deepmind/deepmind-research
공식 코드Unsupervised Learning of Object Keypoints for Perception and Control
https://github.com/deepmind/deepmind-research/tree/master/mmv
공식 코드https://github.com/deepsota/astfocus
공식 코드Efficient Robustness Assessment via Adversarial Spatial-Temporal Focus on Videos
https://github.com/derekpowell/taxi
공식 코드TAXI: Evaluating Categorical Knowledge Editing for Language Models
https://github.com/desehuileng0o0/ikem
공식 코드Elevating Skeleton-Based Action Recognition with Efficient Multi-Modality Self-Supervision
https://github.com/dibschat/openvocab-egoAR
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.