https://github.com/snap-research/panda-70m
Panda-70M: Captioning 70M Videos with Multiple Cross-Modality Teachers
Papers with Code 아카이브
Video Captioning
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 6
- 평가표
- 13
- 보존된 평가 기록
- 86
Video Captioning is a task of automatic captioning a video by understanding the action and event in the video which can help in the retrieval of the video efficiently through text.
Source: NITS-VC System for VATEX Video Captioning Challenge 2020
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/sudatta0993/Dynamic-Congestion-Prediction
https://github.com/tanveer81/rgnet
https://github.com/towhee-io/towhee
Frozen in Time: A Joint Video and Image Encoder for End-to-End Retrieval
https://github.com/ttengwang/ESGN
https://github.com/ubc-vision/make-a-story
MUGEN: A Playground for Video-Audio-Text Multimodal Understanding and GENeration
https://github.com/v-iashin/video_features
https://github.com/voxmenthe/OmniNet_1
OmniNet: A unified architecture for multi-modal multi-task learning
https://github.com/whwu95/BIKE
Cap4Video: What Can Auxiliary Captions Do for Text-Video Retrieval?
https://github.com/whwu95/GPT4Vis
Cap4Video: What Can Auxiliary Captions Do for Text-Video Retrieval?
https://github.com/whwu95/text4vis
Cap4Video: What Can Auxiliary Captions Do for Text-Video Retrieval?
https://github.com/willard-yuan/video-text-retrieval-papers
Frozen in Time: A Joint Video and Image Encoder for End-to-End Retrieval
https://github.com/wqliu657/UniVL
UniVL: A Unified Video and Language Pre-Training Model for Multimodal Understanding and Generation
https://github.com/x-plug/mplug-owl
mPLUG-2: A Modularized Multi-modal Foundation Model Across Text, Image and Video
https://github.com/yangbang18/clfm
https://github.com/yaoli/arctic-capgen-vid
https://github.com/yuweijiang/HybridNet
Video2Commonsense: Generating Commonsense Descriptions to Enrich Video Captioning
https://gitlab.com/grounded-sport-convai/goal-baselines
HERO: Hierarchical Encoder for Video+Language Omni-representation Pre-training
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.