Papers with Code 아카이브
Temporal Action Localization
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
- 14
- 보존된 평가 기록
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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/wuli55555/issf
공식 코드Weakly-Supervised Temporal Action Localization by Inferring Salient Snippet-Feature
https://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/wzzheng/genad
공식 코드https://github.com/wzzheng/gpd
공식 코드https://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/xhaughearl/haarmodic
공식 코드Multi-Resolution Haar Network: Enhancing human motion prediction via Haar transform
https://github.com/xiandaguo/drive-mllm
공식 코드https://github.com/xiaogangpeng/socialtgcn
공식 코드The MI-Motion Dataset and Benchmark for 3D Multi-Person Motion Prediction
https://github.com/xiaojiegu/ultraedit
공식 코드UltraEdit: Training-, Subject-, and Memory-Free Lifelong Editing in Large Language Models
https://github.com/xiaojuntang22/iccv2023-ddgnet
공식 코드DDG-Net: Discriminability-Driven Graph Network for Weakly-supervised Temporal Action Localization
https://github.com/xincoder/GRIP
공식 코드GRIP++: Enhanced Graph-based Interaction-aware Trajectory Prediction for Autonomous Driving
https://github.com/xingli1012/sequentialpointnet
공식 코드Real-time 3D human action recognition based on Hyperpoint sequence
https://github.com/xinhaoyi/edit_bio_long_tail
공식 코드https://github.com/xlliu7/E2E-TAD
공식 코드https://github.com/xlliu7/TadTR
공식 코드https://github.com/xlliu7/muses
공식 코드https://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/xrenaa/safety-aware-motion-prediction
공식 코드Safety-aware Motion Prediction with Unseen Vehicles for Autonomous Driving
https://github.com/xushenlz/parksim
공식 코드https://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/xxh43/ukmd
공식 코드Unsupervised Kinematic Motion Detection for Part-segmented 3D Shape Collections
https://github.com/y-zheng18/gimo
공식 코드https://github.com/yandex-research/shifts
공식 코드Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Tasks
https://github.com/yangle15/DyFADet-pytorch
공식 코드DyFADet: Dynamic Feature Aggregation for Temporal Action Detection
https://github.com/yangzhenkui/witalk
공식 코드Talk is Not Always Cheap: Promoting Wireless Sensing Models with Text Prompts
https://github.com/yanhui002/video_adv
공식 코드https://github.com/yaohui120/comprehendedit
공식 코드ComprehendEdit: A Comprehensive Dataset and Evaluation Framework for Multimodal Knowledge Editing
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.