https://github.com/raghavgoyal14/minotaur/tree/rgv/all-tasks
공식 코드MINOTAUR: Multi-task Video Grounding From Multimodal Queries
Papers with Code 아카이브
Video Grounding
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 2
- 평가표
- 2
- 보존된 평가 기록
- 9
Video grounding is the task of linking spoken language descriptions to specific video segments. In video grounding, the model is given a video and a natural language description, such as a sentence or a caption, and its goal is to identify the specific segment of the video that corresponds to the description. This can involve tasks such as localizing the objects or actions mentioned in the description within the video, or associating a specific time interval with the description.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/rd20karim/synch-transformer
공식 코드Transformer with Controlled Attention for Synchronous Motion Captioning
https://github.com/sangminwoo/explore-and-match
공식 코드https://github.com/sming256/timeloc
공식 코드TimeLoc: A Unified End-to-End Framework for Precise Timestamp Localization in Long Videos
https://github.com/sunoh-kim/pps
공식 코드https://github.com/tencentarc/umt
공식 코드UMT: Unified Multi-modal Transformers for Joint Video Moment Retrieval and Highlight Detection
https://github.com/tzhhhh123/HC-STVG
공식 코드Human-centric Spatio-Temporal Video Grounding With Visual Transformers
https://github.com/waybarrios/guidance-based-video-grounding
공식 코드https://github.com/wjun0830/qd-detr
공식 코드Query-Dependent Video Representation for Moment Retrieval and Highlight Detection
https://github.com/www-ye/timezero
공식 코드TimeZero: Temporal Video Grounding with Reasoning-Guided LVLM
https://github.com/xian-sh/unisdnet
공식 코드Unified Static and Dynamic Network: Efficient Temporal Filtering for Video Grounding
https://github.com/yellow-binary-tree/hawkeye
공식 코드HawkEye: Training Video-Text LLMs for Grounding Text in Videos
https://github.com/yellow-binary-tree/mmduet
공식 코드https://github.com/yisen-feng/osgnet
공식 코드https://github.com/yuxuan-w/geb-plus
공식 코드GEB+: A Benchmark for Generic Event Boundary Captioning, Grounding and Retrieval
https://github.com/zijialewislu/cvpr2025-decafnet
공식 코드DeCafNet: Delegate and Conquer for Efficient Temporal Grounding in Long Videos
https://github.com/zinengtang/VidLanKD
공식 코드VidLanKD: Improving Language Understanding via Video-Distilled Knowledge Transfer
https://github.com/zjuchenlong/wsag
공식 코드https://github.com/MS-P3/code7/tree/main/umt5
UMT: Unified Multi-modal Transformers for Joint Video Moment Retrieval and Highlight Detection
https://github.com/MindCode-4/code-5/tree/main/umt5
UMT: Unified Multi-modal Transformers for Joint Video Moment Retrieval and Highlight Detection
https://github.com/Soldelli/VLG-Net
VLG-Net: Video-Language Graph Matching Network for Video Grounding
https://github.com/WuJie1010/Temporally-language-grounding
https://github.com/aim3-ruc/youmakeup_challenge2022
Negative Sample Matters: A Renaissance of Metric Learning for Temporal Grounding
https://github.com/alvin-zeng/drn
https://github.com/happyharrycn/actionformer_release
https://github.com/jayleicn/moment_detr
https://github.com/shaohuadong2021/dplnet
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.