Action Recognition
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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/wang-chen/LGL
공식 코드https://github.com/wang-chen/lgl-action-recognition
공식 코드https://github.com/wangbing1416/hami-m3d
공식 코드Harmfully Manipulated Images Matter in Multimodal Misinformation Detection
https://github.com/wangjk666/omnivid
공식 코드OmniVid: A Generative Framework for Universal Video Understanding
https://github.com/wangjs96/graph-in-graph-neural-network
공식 코드https://github.com/wanglimin/ARTNet
공식 코드https://github.com/wanglimin/UntrimmedNet
공식 코드UntrimmedNets for Weakly Supervised Action Recognition and Detection
https://github.com/wangyu-ustc/largescalewashing
공식 코드https://github.com/wangyu-ustc/memoryllm
공식 코드https://github.com/wangywust/deepedit
공식 코드https://github.com/wanliyoung/collapse-in-model-editing
공식 코드The Butterfly Effect of Model Editing: Few Edits Can Trigger Large Language Models Collapse
https://github.com/wanliyoung/revisit-editing-evaluation
공식 코드The Mirage of Model Editing: Revisiting Evaluation in the Wild
https://github.com/watonomous/action-classification
공식 코드RALACs: Action Recognition in Autonomous Vehicles using Interaction Encoding and Optical Flow
https://github.com/wei-tim/YOWO
공식 코드You Only Watch Once: A Unified CNN Architecture for Real-Time Spatiotemporal Action Localization
https://github.com/weichuanw/llm-based-mt-via-model-editing
공식 코드https://github.com/weiyifan1023/RaKE
공식 코드Assessing Knowledge Editing in Language Models via Relation Perspective
https://github.com/weiyifan1023/knowledge-edit-based-on-relation-perspective
공식 코드Assessing Knowledge Editing in Language Models via Relation Perspective
https://github.com/wenbohuang1002/soap
공식 코드https://github.com/wengwanjiang/usdrl
공식 코드USDRL: Unified Skeleton-Based Dense Representation Learning with Multi-Grained Feature Decorrelation
https://github.com/wengzejia1/open-vclip
공식 코드Open-VCLIP: Transforming CLIP to an Open-vocabulary Video Model via Interpolated Weight Optimization
https://github.com/wenhanwu95/freqmixformer
공식 코드Frequency Guidance Matters: Skeletal Action Recognition by Frequency-Aware Mixed Transformer
https://github.com/wgcban/apt
공식 코드https://github.com/whwu95/ATM
공식 코드What Can Simple Arithmetic Operations Do for Temporal Modeling?
https://github.com/whwu95/BIKE
공식 코드https://github.com/whwu95/DSANet
공식 코드DSANet: Dynamic Segment Aggregation Network for Video-Level Representation Learning
https://github.com/whwu95/MVFNet
공식 코드MVFNet: Multi-View Fusion Network for Efficient Video Recognition
https://github.com/whwu95/text4vis
공식 코드Revisiting Classifier: Transferring Vision-Language Models for Video Recognition
https://github.com/why986/VFA
공식 코드Versatile Face Animator: Driving Arbitrary 3D Facial Avatar in RGBD Space
https://github.com/wiktormucha/SHARP
공식 코드https://github.com/wiktormucha/effhandegonet
공식 코드In My Perspective, In My Hands: Accurate Egocentric 2D Hand Pose and Action Recognition
https://github.com/willprice/play-fair
공식 코드https://github.com/wjchaoGit/Group-Activity-Recognition
공식 코드Learning Actor Relation Graphs for Group Activity Recognition
https://github.com/wlin-at/cycda
공식 코드CycDA: Unsupervised Cycle Domain Adaptation from Image to Video
https://github.com/wlin-at/maxi
공식 코드https://github.com/wlin-at/vitta
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.