https://github.com/showlab/demovlp
공식 코드Revitalize Region Feature for Democratizing Video-Language Pre-training of Retrieval
Papers with Code 아카이브
Text to Video Retrieval
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 1
- 평가표
- 3
- 보존된 평가 기록
- 4
She's gone I can't find her anywhere I'm looking everywhere for her Everywhere is dark
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/songpipi/amdnet
공식 코드Towards Efficient Partially Relevant Video Retrieval with Active Moment Discovering
https://github.com/tencentarc/mcq
공식 코드Bridging Video-text Retrieval with Multiple Choice Questions
https://github.com/trunpm/pmt-aaai23
공식 코드Efficient End-to-End Video Question Answering with Pyramidal Multimodal Transformer
https://github.com/tsujuifu/pytorch_empirical-mvm
공식 코드An Empirical Study of End-to-End Video-Language Transformers with Masked Visual Modeling
https://github.com/tsujuifu/pytorch_violet
공식 코드VIOLET : End-to-End Video-Language Transformers with Masked Visual-token Modeling
https://github.com/wengzejia1/open-vclip
공식 코드Building an Open-Vocabulary Video CLIP Model with Better Architectures, Optimization and Data
https://github.com/willyfh/msvd-indonesian
공식 코드MSVD-Indonesian: A Benchmark for Multimodal Video-Text Tasks in Indonesian
https://github.com/yuxuan-w/geb-plus
공식 코드GEB+: A Benchmark for Generic Event Boundary Captioning, Grounding and Retrieval
https://github.com/zengyan-97/x2-vlm
공식 코드X$^2$-VLM: All-In-One Pre-trained Model For Vision-Language Tasks
https://github.com/ziyang412/ucofia
공식 코드https://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-9/tree/main/vat
VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text
https://github.com/akashe/ProgrammingInterview
VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text
https://github.com/albanie/collaborative-experts
The End-of-End-to-End: A Video Understanding Pentathlon Challenge (2020)
https://github.com/antoine77340/S3D_HowTo100M
HowTo100M: Learning a Text-Video Embedding by Watching Hundred Million Narrated Video Clips
https://github.com/antoine77340/milnce_howto100m
HowTo100M: Learning a Text-Video Embedding by Watching Hundred Million Narrated Video Clips
https://github.com/facebookresearch/EgoTV
CLIP4Clip: An Empirical Study of CLIP for End to End Video Clip Retrieval
https://github.com/linjieli222/hero_video_feature_extractor
End-to-End Learning of Visual Representations from Uncurated Instructional Videos
https://github.com/m-bain/CondensedMovies
Condensed Movies: Story Based Retrieval with Contextual Embeddings
https://github.com/m-bain/webvid
Frozen in Time: A Joint Video and Image Encoder for End-to-End Retrieval
https://github.com/princetonvisualai/mqvr
Frozen in Time: A Joint Video and Image Encoder for End-to-End Retrieval
https://github.com/pwc-1/Paper-9/tree/main/3/vat
VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text
https://github.com/roudimit/AVLnet
HowTo100M: Learning a Text-Video Embedding by Watching Hundred Million Narrated Video Clips
https://github.com/stanfordvl/atp-video-language
https://github.com/towhee-io/towhee
MDMMT: Multidomain Multimodal Transformer for Video Retrieval
https://github.com/willard-yuan/video-text-retrieval-papers
MDMMT: Multidomain Multimodal Transformer for Video Retrieval
https://github.com/zengyan-97/x-vlm
X$^2$-VLM: All-In-One Pre-trained Model For Vision-Language Tasks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.