https://github.com/zgzxy001/stmt
공식 코드STMT: A Spatial-Temporal Mesh Transformer for MoCap-Based Action Recognition
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/zh460045050/SNL_ICCV2021
공식 코드https://github.com/zhang-can/PAN-PyTorch
공식 코드PAN: Towards Fast Action Recognition via Learning Persistence of Appearance
https://github.com/zhang-yihao/adversarial-representation-engineering
공식 코드Adversarial Representation Engineering: A General Model Editing Framework for Large Language Models
https://github.com/zhanghaotong1/wslvideodenseanticipation
공식 코드https://github.com/zhanglab-deepneurocoglab/motionperceiver
공식 코드Flow Snapshot Neurons in Action: Deep Neural Networks Generalize to Biological Motion Perception
https://github.com/zhaoyue-zephyrus/avion
공식 코드https://github.com/zhaoyue-zephyrus/testra
공식 코드Real-time Online Video Detection with Temporal Smoothing Transformers
https://github.com/zhaozetong/dualedit
공식 코드Mitigating Safety Fallback in Editing-based Backdoor Injection on LLMs
https://github.com/zhenfeiz/cimd
공식 코드A New Benchmark and Model for Challenging Image Manipulation Detection
https://github.com/zhengshou/scnn
공식 코드Temporal Action Localization in Untrimmed Videos via Multi-stage CNNs
https://github.com/zhihengli-UR/DebiAN
공식 코드Discover and Mitigate Unknown Biases with Debiasing Alternate Networks
https://github.com/zhimengj0326/gradient_rewiring_editing
공식 코드Gradient Rewiring for Editable Graph Neural Network Training
https://github.com/zhongping-zhang/movie-clip
공식 코드Movie Genre Classification by Language Augmentation and Shot Sampling
https://github.com/zhoubenjia/motionrgbd-pami
공식 코드A Unified Multimodal De- and Re-coupling Framework for RGB-D Motion Recognition
https://github.com/zhoukanglei/cofinal_aqa
공식 코드CoFInAl: Enhancing Action Quality Assessment with Coarse-to-Fine Instruction Alignment
https://github.com/zhoukanglei/phi_aqa
공식 코드https://github.com/zhouyuxuanyx/blockgcn
공식 코드https://github.com/zhrli324/rledit
공식 코드https://github.com/zhujiagang/DTPP
공식 코드End-to-end Video-level Representation Learning for Action Recognition
https://github.com/zhujiagang/gating-ConvNet-code
공식 코드Learning Gating ConvNet for Two-Stream based Methods in Action Recognition
https://github.com/zhuoxiao-chen/cevt
공식 코드Conditional Extreme Value Theory for Open Set Video Domain Adaptation
https://github.com/zhuxinqimac/abm-pytorch
공식 코드https://github.com/zhysora/fr-head
공식 코드Learning Discriminative Representations for Skeleton Based Action Recognition
https://github.com/ziqipang/lm4visualencoding
공식 코드Frozen Transformers in Language Models Are Effective Visual Encoder Layers
https://github.com/zjunlp/BiasEdit
공식 코드BiasEdit: Debiasing Stereotyped Language Models via Model Editing
https://github.com/zjunlp/PromptKG/tree/main/deltaKG
공식 코드https://github.com/zjunlp/cake
공식 코드CaKE: Circuit-aware Editing Enables Generalizable Knowledge Learners
https://github.com/zjunlp/easyedit
공식 코드Editing Large Language Models: Problems, Methods, and Opportunities
https://github.com/zjunlp/knowledgecircuits
공식 코드https://github.com/zjunlp/knowledgeeditingpapers
공식 코드A Comprehensive Study of Knowledge Editing for Large Language Models
https://github.com/zjunlp/knowlm
공식 코드EasyEdit: An Easy-to-use Knowledge Editing Framework for Large Language Models
https://github.com/zjunlp/oneedit
공식 코드OneEdit: A Neural-Symbolic Collaboratively Knowledge Editing System
https://github.com/zjunlp/pitfallsknowledgeediting
공식 코드Unveiling the Pitfalls of Knowledge Editing for Large Language Models
https://github.com/zjunlp/promptkg
공식 코드https://github.com/znaoya/aenet
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.