https://github.com/knmac/adaptive_spatiotemporal
공식 코드Efficient Human Vision Inspired Action Recognition using Adaptive Spatiotemporal Sampling
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/kpeng9510/featfsda
공식 코드Exploring Few-Shot Adaptation for Activity Recognition on Diverse Domains
https://github.com/kpeng9510/hopadiff
공식 코드https://github.com/kpeng9510/musclemap
공식 코드https://github.com/kpeng9510/os-sar
공식 코드Navigating Open Set Scenarios for Skeleton-based Action Recognition
https://github.com/kpeng9510/proformer
공식 코드Delving Deep into One-Shot Skeleton-based Action Recognition with Diverse Occlusions
https://github.com/kpeng9510/ravar
공식 코드https://github.com/kpeng9510/relamix
공식 코드Exploring Few-Shot Adaptation for Activity Recognition on Diverse Domains
https://github.com/kpeng9510/trans4soar
공식 코드Delving Deep into One-Shot Skeleton-based Action Recognition with Diverse Occlusions
https://github.com/kpeng9510/transdarc
공식 코드TransDARC: Transformer-based Driver Activity Recognition with Latent Space Feature Calibration
https://github.com/kracwarlock/action-recognition-visual-attention
공식 코드https://github.com/krystalan/bi_zsre
공식 코드https://github.com/kuangenzhang/edh
공식 코드Ensemble diverse hypotheses and knowledge distillation for unsupervised cross-subject adaptation
https://github.com/kuangzijian/Improved-Actor-Relation-Graph-based-Group-Activity-Recognition
공식 코드Improved Actor Relation Graph based Group Activity Recognition
https://github.com/kunli-cs/pcan
공식 코드Prototypical Calibrating Ambiguous Samples for Micro-Action Recognition
https://github.com/kunyulin/xov-action
공식 코드Rethinking CLIP-based Video Learners in Cross-Domain Open-Vocabulary Action Recognition
https://github.com/kwanwaichung/jotr
공식 코드JoTR: A Joint Transformer and Reinforcement Learning Framework for Dialog Policy Learning
https://github.com/kylehuang9/sefar
공식 코드https://github.com/kylemin/Gaze-Attention
공식 코드Integrating Human Gaze into Attention for Egocentric Activity Recognition
https://github.com/lab-emi/airhar
공식 코드https://github.com/lalasray/txp
공식 코드https://github.com/lambert-x/video-semisup
공식 코드Learning from Temporal Gradient for Semi-supervised Action Recognition
https://github.com/lamda-rl/act
공식 코드ACT: Empowering Decision Transformer with Dynamic Programming via Advantage Conditioning
https://github.com/lanfeng4659/knowledgeminingwithscenetext
공식 코드Knowledge Mining with Scene Text for Fine-Grained Recognition
https://github.com/laura-wang/video-pace
공식 코드Self-supervised Video Representation Learning by Pace Prediction
https://github.com/laura-wang/video_repres_mas
공식 코드https://github.com/laura-wang/video_repres_sts
공식 코드Self-supervised Video Representation Learning by Uncovering Spatio-temporal Statistics
https://github.com/laurenok24/nsaqa
공식 코드Hierarchical NeuroSymbolic Approach for Comprehensive and Explainable Action Quality Assessment
https://github.com/ldarrick/PathSignatures
공식 코드https://github.com/ldkong1205/transvae
공식 코드Unsupervised Video Domain Adaptation for Action Recognition: A Disentanglement Perspective
https://github.com/leaplabthu/dynamic_perceiver
공식 코드https://github.com/leesb7426/team
공식 코드Temporal Alignment-Free Video Matching for Few-shot Action Recognition
https://github.com/leexinhao/ZeroI2V
공식 코드ZeroI2V: Zero-Cost Adaptation of Pre-trained Transformers from Image to Video
https://github.com/leiwangr/udtw
공식 코드https://github.com/leiwangr/video-ar
공식 코드https://github.com/leo-liuzy/propmend
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.