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/jackyjsy/CVPR21Chal-SLR
공식 코드https://github.com/jackyjsy/sam-slr-v2
공식 코드Sign Language Recognition via Skeleton-Aware Multi-Model Ensemble
https://github.com/jacobchalk/tim
공식 코드TIM: A Time Interval Machine for Audio-Visual Action Recognition
https://github.com/jalayrac/object-states-action
공식 코드https://github.com/jasonforjoy/model-editing-hurt
공식 코드Model Editing Harms General Abilities of Large Language Models: Regularization to the Rescue
https://github.com/jaulin-bage/task-adapter-pp
공식 코드Task-Adapter++: Task-specific Adaptation with Order-aware Alignment for Few-shot Action Recognition
https://github.com/jayleicn/singularity
공식 코드https://github.com/jeffhernandez1995/vic-mae
공식 코드https://github.com/jemoka/dropfree
공식 코드https://github.com/jfzhang95/pytorch-video-recognition
공식 코드https://github.com/jhang2020/pcm3
공식 코드Self-Supervised Skeleton-Based Action Representation Learning: A Benchmark and Beyond
https://github.com/jhong93/vpd
공식 코드Video Pose Distillation for Few-Shot, Fine-Grained Sports Action Recognition
https://github.com/jiaangl/elder
공식 코드ELDER: Enhancing Lifelong Model Editing with Mixture-of-LoRA
https://github.com/jiakuan929/superficial-editing
공식 코드Revealing the Deceptiveness of Knowledge Editing: A Mechanistic Analysis of Superficial Editing
https://github.com/jianghoucheng/alphaedit
공식 코드AlphaEdit: Null-Space Constrained Knowledge Editing for Language Models
https://github.com/jianghoucheng/nse
공식 코드https://github.com/jianglongye/cgf
공식 코드Learning Continuous Grasping Function with a Dexterous Hand from Human Demonstrations
https://github.com/jiany-ctrl/Crossmodal-KD-for-Vision-to-Sensor-HAR
공식 코드Cross-modal Knowledge Distillation for Vision-to-Sensor Action Recognition
https://github.com/jiaozizhao/Two-in-One-ActionDetection
공식 코드https://github.com/jiazheng-xing/gghm
공식 코드Boosting Few-shot Action Recognition with Graph-guided Hybrid Matching
https://github.com/jinglinglingling/x4d
공식 코드https://github.com/jingyaojade/SpSt-GCN
공식 코드Skeleton-Based Action Recognition with Spatial-Structural Graph Convolution
https://github.com/jiyanggao/TURN-TAP
공식 코드TURN TAP: Temporal Unit Regression Network for Temporal Action Proposals
https://github.com/jkini/Meccano
공식 코드https://github.com/jnnan/chairs
공식 코드https://github.com/john-hewitt/model-editing-canonical-examples
공식 코드https://github.com/johnnychanv/JailbreakEdit
공식 코드Injecting Universal Jailbreak Backdoors into LLMs in Minutes
https://github.com/jometeorie/multihopshortcuts
공식 코드Investigating Multi-Hop Factual Shortcuts in Knowledge Editing of Large Language Models
https://github.com/joslefaure/hit
공식 코드Holistic Interaction Transformer Network for Action Detection
https://github.com/ju-chen/Efficient-Prompt
공식 코드Prompting Visual-Language Models for Efficient Video Understanding
https://github.com/juliendenize/eztorch
공식 코드Similarity Contrastive Estimation for Image and Video Soft Contrastive Self-Supervised Learning
https://github.com/jun-cen/psl
공식 코드Enlarging Instance-specific and Class-specific Information for Open-set Action Recognition
https://github.com/junweiliang/multitrain
공식 코드Multi-dataset Training of Transformers for Robust Action Recognition
https://github.com/jx-zhong-for-academic-purpose/kinet
공식 코드https://github.com/kaai520/PGFA
공식 코드Zero-shot Skeleton-based Action Recognition with Prototype-guided Feature Alignment
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.