https://github.com/why986/VFA
공식 코드Versatile Face Animator: Driving Arbitrary 3D Facial Avatar in RGBD Space
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/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 코드https://github.com/wonderful-team-robotics/wonderful_team_robotics
공식 코드Wonderful Team: Zero-Shot Physical Task Planning with Visual LLMs
https://github.com/wuchengyuan88/room-occupancy-topology
공식 코드https://github.com/wuzhenyusjtu/Privacy-AdversarialLearning
공식 코드Towards Privacy-Preserving Visual Recognition via Adversarial Training: A Pilot Study
https://github.com/wzmsltw/BSN-boundary-sensitive-network.pytorch
공식 코드BSN: Boundary Sensitive Network for Temporal Action Proposal Generation
https://github.com/xacheng1996/sllm
공식 코드Sample Less, Learn More: Efficient Action Recognition via Frame Feature Restoration
https://github.com/xbmxb/edit_analysis
공식 코드https://github.com/xh20/HAGCN
공식 코드Skeletal Human Action Recognition using Hybrid Attention based Graph Convolutional Network
https://github.com/xiaobai1217/DomainAdaptation
공식 코드https://github.com/xiaojiegu/ultraedit
공식 코드UltraEdit: Training-, Subject-, and Memory-Free Lifelong Editing in Large Language Models
https://github.com/xingaoli/dp-hoi
공식 코드Disentangled Pre-training for Human-Object Interaction Detection
https://github.com/xingli1012/sequentialpointnet
공식 코드Real-time 3D human action recognition based on Hyperpoint sequence
https://github.com/xingyu-song/4a
공식 코드An Animation-based Augmentation Approach for Action Recognition from Discontinuous Video
https://github.com/xingyul/cpnet
공식 코드Learning Video Representations from Correspondence Proposals
https://github.com/xingyul/meteornet
공식 코드MeteorNet: Deep Learning on Dynamic 3D Point Cloud Sequences
https://github.com/xinhaoyi/edit_bio_long_tail
공식 코드https://github.com/xinyusun/mme
공식 코드Masked Motion Encoding for Self-Supervised Video Representation Learning
https://github.com/xliu443/language-free-compositional-action-generation-via-decoupling-refinement
공식 코드Language-free Compositional Action Generation via Decoupling Refinement
https://github.com/xpq-tech/code-llmedit
공식 코드https://github.com/xpq-tech/keti
공식 코드Identifying Knowledge Editing Types in Large Language Models
https://github.com/xpq-tech/pmet
공식 코드https://github.com/xpq-tech/swea
공식 코드SWEA: Updating Factual Knowledge in Large Language Models via Subject Word Embedding Altering
https://github.com/xuboshen/egoncepp
공식 코드EgoNCE++: Do Egocentric Video-Language Models Really Understand Hand-Object Interactions?
https://github.com/xueyee/groupformer
공식 코드GroupFormer: Group Activity Recognition with Clustered Spatial-Temporal Transformer
https://github.com/xuxy09/RSC-Net
공식 코드3D Human Shape and Pose from a Single Low-Resolution Image with Self-Supervised Learning
https://github.com/xuyankun/stvig-for-reads-v
공식 코드VSViG: Real-time Video-based Seizure Detection via Skeleton-based Spatiotemporal ViG
https://github.com/xuyankun/vsvig
공식 코드VSViG: Real-time Video-based Seizure Detection via Skeleton-based Spatiotemporal ViG
https://github.com/xuyizdby/noiseerasar
공식 코드https://github.com/xuyu0010/atcon
공식 코드Source-free Video Domain Adaptation by Learning Temporal Consistency for Action Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.