https://github.com/zgzxy001/stmt
공식 코드STMT: A Spatial-Temporal Mesh Transformer for MoCap-Based Action Recognition
Papers with Code 아카이브
Temporal Action Localization
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 8
- 평가표
- 14
- 보존된 평가 기록
- 125
Temporal Action Localization aims to detect activities in the video stream and output beginning and end timestamps. It is closely related to Temporal Action Proposal Generation.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/zhang-can/CoLA
공식 코드CoLA: Weakly-Supervised Temporal Action Localization with Snippet Contrastive Learning
https://github.com/zhang-can/up-tal
공식 코드Unsupervised Pre-training for Temporal Action Localization Tasks
https://github.com/zhang-yihao/adversarial-representation-engineering
공식 코드Adversarial Representation Engineering: A General Model Editing Framework for Large Language Models
https://github.com/zhangyp15/beverse
공식 코드BEVerse: Unified Perception and Prediction in Birds-Eye-View for Vision-Centric Autonomous Driving
https://github.com/zhaoxing2022/mmn-vsod
공식 코드Motion-aware Memory Network for Fast Video Salient Object Detection
https://github.com/zhaozetong/dualedit
공식 코드Mitigating Safety Fallback in Editing-based Backdoor Injection on LLMs
https://github.com/zhejz/hptr
공식 코드Real-Time Motion Prediction via Heterogeneous Polyline Transformer with Relative Pose Encoding
https://github.com/zhejz/trafficbots
공식 코드TrafficBots: Towards World Models for Autonomous Driving Simulation and Motion Prediction
https://github.com/zhenfeiz/cimd
공식 코드A New Benchmark and Model for Challenging Image Manipulation Detection
https://github.com/zhengshou/AutoLoc
공식 코드https://github.com/zhengshou/scnn
공식 코드Temporal Action Localization in Untrimmed Videos via Multi-stage CNNs
https://github.com/zhimengj0326/gradient_rewiring_editing
공식 코드Gradient Rewiring for Editable Graph Neural Network Training
https://github.com/zhou745/gaufuse_wstal
공식 코드Improving Weakly Supervised Temporal Action Localization by Bridging Train-Test Gap in Pseudo Labels
https://github.com/zhouyang-001/smbg-for-temporal-action-proposal
공식 코드Faster Learning of Temporal Action Proposal via Sparse Multilevel Boundary Generator
https://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/zhysora/fr-head
공식 코드Learning Discriminative Representations for Skeleton Based Action Recognition
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/zshn25/pc4consistentdepth
공식 코드Pose Constraints for Consistent Self-supervised Monocular Depth and Ego-motion
https://github.com/zympsyche/bevworld
공식 코드BEVWorld: A Multimodal World Model for Autonomous Driving via Unified BEV Latent Space
https://github.com/zzhao71/ripplecot
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.