Papers with Code 아카이브
Video Retrieval
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 5
- 평가표
- 19
- 보존된 평가 기록
- 308
The objective of video retrieval is as follows: given a text query and a pool of candidate videos, select the video which corresponds to the text query. Typically, the videos are returned as a ranked list of candidates and scored via document retrieval metrics.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/yimuwangcs/Better_Cross_Modal_Retrieval
공식 코드Video-Text Retrieval by Supervised Sparse Multi-Grained Learning
https://github.com/yisen-feng/osgnet
공식 코드https://github.com/yuanli2333/Hadamard-Matrix-for-hashing
공식 코드Central Similarity Quantization for Efficient Image and Video Retrieval
https://github.com/yuanyao366/PRP
공식 코드Video Playback Rate Perception for Self-supervisedSpatio-Temporal Representation Learning
https://github.com/yuqi657/ts2_net
공식 코드TS2-Net: Token Shift and Selection Transformer for Text-Video Retrieval
https://github.com/yuxuan-w/geb-plus
공식 코드GEB+: A Benchmark for Generic Event Boundary Captioning, Grounding and Retrieval
https://github.com/yyuncong/tempclr
공식 코드TempCLR: Temporal Alignment Representation with Contrastive Learning
https://github.com/zchoi/glscl
공식 코드Text-Video Retrieval with Global-Local Semantic Consistent Learning
https://github.com/zengyan-97/x2-vlm
공식 코드X$^2$-VLM: All-In-One Pre-trained Model For Vision-Language Tasks
https://github.com/zhangbw17/mv-adapter
공식 코드MV-Adapter: Multimodal Video Transfer Learning for Video Text 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/zinengtang/tvlt
공식 코드https://github.com/ziyang412/ucofia
공식 코드https://github.com/zjuchenlong/wsag
공식 코드https://github.com/BestJuly/IIC
Self-supervised Video Representation Learning Using Inter-intra Contrastive Framework
https://github.com/BestJuly/VCP
Video Cloze Procedure for Self-Supervised Spatio-Temporal Learning
https://github.com/Chaolei98/FreeZAD
CoCa: Contrastive Captioners are Image-Text Foundation Models
https://github.com/HanNight/RE-T5
https://github.com/Hangz-nju-cuhk/Talking-Face-Generation-DAVS
Talking Face Generation by Adversarially Disentangled Audio-Visual Representation
https://github.com/LADI-Dataset/ladi-overview
https://github.com/LavieLuo/DRA
Discriminative Residual Analysis for Image Set Classification with Posture and Age Variations
https://github.com/MKLab-ITI/FIVR-200K
https://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-1/tree/main/tvlt
https://github.com/MindCode-4/code-13/tree/main/vat
VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text
https://github.com/MindCode-4/code-3/tree/main/florence2
https://github.com/MindCode-4/code-5/tree/main/tvlt
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/MindCode-4/code-5/tree/main/x_clip
X-CLIP: End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval
https://github.com/MindCode-4/code-9/tree/main/vat
VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text
https://github.com/MindSpore-paper-code-3/code8/tree/main/ecolite
ECO: Efficient Convolutional Network for Online Video Understanding
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/NeosXu/benchmarking_pytorchvideo_and_mmaction2
Hysia: Serving DNN-Based Video-to-Retail Applications in Cloud
https://github.com/PKU-YuanGroup/LLMBind
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.