https://github.com/Guaranteer/VidSTG-Dataset
공식 코드Where Does It Exist: Spatio-Temporal Video Grounding for Multi-Form Sentences
Papers with Code 아카이브
Video Grounding
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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/HengLan/TA-STVG
공식 코드Knowing Your Target: Target-Aware Transformer Makes Better Spatio-Temporal Video Grounding
https://github.com/SUTDCV/Animal-Kingdom
공식 코드Animal Kingdom: A Large and Diverse Dataset for Animal Behavior Understanding
https://github.com/SichengMo/Ego4D_NLQ_Actionformer
공식 코드A Simple Transformer-Based Model for Ego4D Natural Language Queries Challenge
https://github.com/antoyang/TubeDETR
공식 코드https://github.com/appletea233/llava-st
공식 코드LLaVA-ST: A Multimodal Large Language Model for Fine-Grained Spatial-Temporal Understanding
https://github.com/appletea233/temporal-r1
공식 코드Reinforcement Learning Tuning for VideoLLMs: Reward Design and Data Efficiency
https://github.com/becomebright/groundvqa
공식 코드https://github.com/bytedance/tarsier
공식 코드https://github.com/doc-doc/next-gqa
공식 코드Can I Trust Your Answer? Visually Grounded Video Question Answering
https://github.com/easonxiao-888/uvcom
공식 코드https://github.com/ericashimomoto/parameter-efficient-tvg
공식 코드Towards Parameter-Efficient Integration of Pre-Trained Language Models In Temporal Video Grounding
https://github.com/fletcherjiang/llmepet
공식 코드Prior Knowledge Integration via LLM Encoding and Pseudo Event Regulation for Video Moment Retrieval
https://github.com/frostinassiky/denoiseloc
공식 코드https://github.com/google-research/scenic
공식 코드https://github.com/google/video-localized-narratives
공식 코드Connecting Vision and Language with Video Localized Narratives
https://github.com/henglan/cgstvg
공식 코드https://github.com/houzhijian/cone
공식 코드CONE: An Efficient COarse-to-fiNE Alignment Framework for Long Video Temporal Grounding
https://github.com/huangb23/vtimellm
공식 코드https://github.com/intel/TVP
공식 코드Text-Visual Prompting for Efficient 2D Temporal Video Grounding
https://github.com/jayaprakash-a/Cross-Modal-learning-for-Audio-Visual-Video-Parsing
공식 코드https://github.com/jellyyao3000/omnistvg
공식 코드OmniSTVG: Toward Spatio-Temporal Omni-Object Video Grounding
https://github.com/jinhyunj/eatr
공식 코드Knowing Where to Focus: Event-aware Transformer for Video Grounding
https://github.com/jy0205/stcat
공식 코드Embracing Consistency: A One-Stage Approach for Spatio-Temporal Video Grounding
https://github.com/lijiaxiuHFUT/DPTMO
공식 코드Dual-Path Temporal Map Optimization for Make-up Temporal Video Grounding
https://github.com/love481/Cross-modal-Contrastive-Learning-with-Asymmetric-Co-attention-Network-for-Video-Moment-Retrieval
공식 코드Cross-modal Contrastive Learning with Asymmetric Co-attention Network for Video Moment Retrieval
https://github.com/marmot-xy/spoken-video-grounding
공식 코드https://github.com/mastervito/diffusionvg
공식 코드Exploring Iterative Refinement with Diffusion Models for Video Grounding
https://github.com/mbzuai-oryx/video-llava
공식 코드https://github.com/mcg-nju/mmn
공식 코드Negative Sample Matters: A Renaissance of Metric Learning for Temporal Grounding
https://github.com/mlvlab/vidchain
공식 코드VidChain: Chain-of-Tasks with Metric-based Direct Preference Optimization for Dense Video Captioning
https://github.com/nanguoshun/IVG
공식 코드Interventional Video Grounding with Dual Contrastive Learning
https://github.com/nevermorelch/ccg
공식 코드Consistency of Compositional Generalization across Multiple Levels
https://github.com/opengvlab/internvideo
공식 코드InternVideo2: Scaling Foundation Models for Multimodal Video Understanding
https://github.com/opengvlab/internvideo2
공식 코드InternVideo2: Scaling Foundation Models for Multimodal Video Understanding
https://github.com/qiujihao19/artemis
공식 코드Artemis: Towards Referential Understanding in Complex Videos
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.