https://github.com/rerv/uniadapter
공식 코드UniAdapter: Unified Parameter-Efficient Transfer Learning for Cross-modal Modeling
Papers with Code 아카이브
Video-Text Retrieval
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Video-Text retrieval requires understanding of both video and language together. Therefore it's different to video retrieval task.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/showlab/egovlp
공식 코드https://github.com/showlab/movieseq
공식 코드https://github.com/starmemda/CAMoE
공식 코드Improving Video-Text Retrieval by Multi-Stream Corpus Alignment and Dual Softmax Loss
https://github.com/starmemda/camow
공식 코드Improving Video-Text Retrieval by Multi-Stream Corpus Alignment and Dual Softmax Loss
https://github.com/tanveer81/rgnet
공식 코드RGNet: A Unified Clip Retrieval and Grounding Network for Long Videos
https://github.com/technomad-ds/lovr-benchmark
공식 코드LoVR: A Benchmark for Long Video Retrieval in Multimodal Contexts
https://github.com/tencentarc/mcq
공식 코드Bridging Video-text Retrieval with Multiple Choice Questions
https://github.com/uniadapter/uniadapter
공식 코드UniAdapter: Unified Parameter-Efficient Transfer Learning for Cross-modal Modeling
https://github.com/wengzejia1/open-vclip
공식 코드Building an Open-Vocabulary Video CLIP Model with Better Architectures, Optimization and Data
https://github.com/woodfrog/vse_infty
공식 코드Learning the Best Pooling Strategy for Visual Semantic Embedding
https://github.com/xid32/naacl_2025_twm
공식 코드Temporal Working Memory: Query-Guided Segment Refinement for Enhanced Multimodal Understanding
https://github.com/xuguohai/X-CLIP
공식 코드X-CLIP: End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval
https://github.com/yimuwangcs/Better_Cross_Modal_Retrieval
공식 코드Video-Text Retrieval by Supervised Sparse Multi-Grained Learning
https://github.com/zhangbw17/mv-adapter
공식 코드MV-Adapter: Multimodal Video Transfer Learning for Video Text Retrieval
https://github.com/ziyang412/ucofia
공식 코드https://github.com/MindCode-4/code-5/tree/main/x_clip
X-CLIP: End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval
https://github.com/MindSpore-scientific/code-7/tree/main/X_CLIP
X-CLIP: End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval
https://github.com/PKU-YuanGroup/LLMBind
https://github.com/PKU-YuanGroup/MoE-LLaVA
https://github.com/PKU-YuanGroup/Video-LLaVA
https://github.com/aranciokov/fsmmda_videoretrieval
Fine-grained Video-Text Retrieval with Hierarchical Graph Reasoning
https://github.com/aranciokov/ranp
Fine-grained Video-Text Retrieval with Hierarchical Graph Reasoning
https://github.com/chendelong1999/subobjects
One Trajectory, One Token: Grounded Video Tokenization via Panoptic Sub-object Trajectory
https://github.com/cshizhe/hgr_v2t
Fine-grained Video-Text Retrieval with Hierarchical Graph Reasoning
https://github.com/damo-nlp-sg/videollama2
Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding
https://github.com/damo-nlp-sg/videollama3
Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding
https://github.com/facebookresearch/EgoTV
CLIP4Clip: An Empirical Study of CLIP for End to End Video Clip Retrieval
https://github.com/lntzm/cvpr24track-longvideo
TimeChat: A Time-sensitive Multimodal Large Language Model for Long Video Understanding
https://github.com/m-bain/webvid
Frozen in Time: A Joint Video and Image Encoder for End-to-End Retrieval
https://github.com/modelscope/modelscope
mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections
https://github.com/opengvlab/internvl-mmdetseg
InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks
https://github.com/pku-yuangroup/video-bench
https://github.com/princetonvisualai/mqvr
Frozen in Time: A Joint Video and Image Encoder for End-to-End Retrieval
https://github.com/roudimit/AVLnet
CLIP4Clip: An Empirical Study of CLIP for End to End Video Clip Retrieval
https://github.com/showlab/region_learner
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.