https://github.com/zhihengli-UR/DebiAN
공식 코드Discover and Mitigate Unknown Biases with Debiasing Alternate Networks
Papers with Code 아카이브
Activity Recognition
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 11
- 평가표
- 4
- 보존된 평가 기록
- 10
Human Activity Recognition is the problem of identifying events performed by humans given a video input. It is formulated as a binary (or multiclass) classification problem of outputting activity class labels. Activity Recognition is an important problem with many societal applications including smart surveillance, video search/retrieval, intelligent robots, and other monitoring systems.
Source: Learning Latent Sub-events in Activity Videos Using Temporal Attention Filters
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
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/zhourobotics/llm-mrs-survey
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 코드https://github.com/zongkaiz/fleke
공식 코드https://github.com/zphyix/tcm
공식 코드Motion-driven Visual Tempo Learning for Video-based Action Recognition
https://github.com/zqiao11/tscil
공식 코드Class-incremental Learning for Time Series: Benchmark and Evaluation
https://github.com/zrrr1997/syn2real_dg
공식 코드https://github.com/zsofiak/masterthesis
공식 코드MARINE: A Computer Vision Model for Detecting Rare Predator-Prey Interactions in Animal Videos
https://github.com/zwx8981/BVQA-2021
공식 코드Blindly Assess Quality of In-the-Wild Videos via Quality-aware Pre-training and Motion Perception
https://github.com/zzhao71/ripplecot
공식 코드https://github.com/zzysteve/skeletonx
공식 코드SkeletonX: Data-Efficient Skeleton-based Action Recognition via Cross-sample Feature Aggregation
https://gitlab.com/MISLgit/forensic-similarity-for-digital-images
공식 코드https://gitlab.com/nct_tso_public/surgvgen
공식 코드Mission Balance: Generating Under-represented Class Samples using Video Diffusion Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.