https://github.com/ArminS03/CAREL
공식 코드CAREL: Instruction-guided reinforcement learning with cross-modal auxiliary objectives
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/ArrowLuo/CLIP4Clip
공식 코드CLIP4Clip: An Empirical Study of CLIP for End to End Video Clip Retrieval
https://github.com/CryhanFang/CLIP2Video
공식 코드https://github.com/FangyunWei/SLRT
공식 코드CiCo: Domain-Aware Sign Language Retrieval via Cross-Lingual Contrastive Learning
https://github.com/Paranioar/UniPT
공식 코드UniPT: Universal Parallel Tuning for Transfer Learning with Efficient Parameter and Memory
https://github.com/Roc-Ng/HANet
공식 코드HANet: Hierarchical Alignment Networks for Video-Text Retrieval
https://github.com/alibaba/AliceMind/tree/main/mPLUG
공식 코드mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections
https://github.com/alipay/Ant-Multi-Modal-Framework
공식 코드https://github.com/bpiyush/TestOfTime
공식 코드Test of Time: Instilling Video-Language Models with a Sense of Time
https://github.com/caryxiang/eca4vtr
공식 코드Expertized Caption Auto-Enhancement for Video-Text Retrieval
https://github.com/chinmayad/motiondescriptions
공식 코드Diving Deep into the Motion Representation of Video-Text Models
https://github.com/chuhanxx/helping_hand_for_egocentric_videos
공식 코드Helping Hands: An Object-Aware Ego-Centric Video Recognition Model
https://github.com/computer-vision-in-the-wild/cvinw_readings
공식 코드Vision-Language Pre-training: Basics, Recent Advances, and Future Trends
https://github.com/damo-nlp-sg/video-llama
공식 코드Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding
https://github.com/fangkaipeng/pros
공식 코드ProS: Prompting-to-simulate Generalized knowledge for Universal Cross-Domain Retrieval
https://github.com/farewellthree/stan
공식 코드Revisiting Temporal Modeling for CLIP-based Image-to-Video Knowledge Transferring
https://github.com/fundamentalvision/Uni-Perceiver
공식 코드Uni-Perceiver-MoE: Learning Sparse Generalist Models with Conditional MoEs
https://github.com/gengyuanmax/mevtr
공식 코드https://github.com/gingsi/coot-videotext
공식 코드COOT: Cooperative Hierarchical Transformer for Video-Text Representation Learning
https://github.com/gxym/stgt
공식 코드Video-Language Alignment via Spatio-Temporal Graph Transformer
https://github.com/iigroup/scl
공식 코드Seeing What You Miss: Vision-Language Pre-training with Semantic Completion Learning
https://github.com/jerryyli/svitt
공식 코드https://github.com/layer6ai-labs/xpool
공식 코드X-Pool: Cross-Modal Language-Video Attention for Text-Video Retrieval
https://github.com/leolee99/pau
공식 코드Prototype-based Aleatoric Uncertainty Quantification for Cross-modal Retrieval
https://github.com/lunarshen/dsicovla
공식 코드https://github.com/m-bain/frozen-in-time
공식 코드Frozen in Time: A Joint Video and Image Encoder for End-to-End Retrieval
https://github.com/microsoft/xpretrain
공식 코드CLIP-ViP: Adapting Pre-trained Image-Text Model to Video-Language Representation Alignment
https://github.com/mlvlab/vid-tldr
공식 코드vid-TLDR: Training Free Token merging for Light-weight Video Transformer
https://github.com/musicman217/Text-Proxy
공식 코드https://github.com/nshubham655/RUDDER
공식 코드https://github.com/opengvlab/internvideo
공식 코드Harvest Video Foundation Models via Efficient Post-Pretraining
https://github.com/opengvlab/internvl
공식 코드InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks
https://github.com/pku-yuangroup/languagebind
공식 코드https://github.com/qyr0403/reversed-in-time
공식 코드Reversed in Time: A Novel Temporal-Emphasized Benchmark for Cross-Modal Video-Text Retrieval
https://github.com/renshuhuai-andy/testa
공식 코드TESTA: Temporal-Spatial Token Aggregation for Long-form Video-Language Understanding
https://github.com/renshuhuai-andy/timechat
공식 코드TimeChat: A Time-sensitive Multimodal Large Language Model for Long Video Understanding
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.