https://github.com/vaew/skyscript-100m
공식 코드SkyScript-100M: 1,000,000,000 Pairs of Scripts and Shooting Scripts for Short Drama
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/valterlej/dvcusi
공식 코드Dense Video Captioning Using Unsupervised Semantic Information
https://github.com/valterlej/zsarcap
공식 코드Tell me what you see: A zero-shot action recognition method based on natural language descriptions
https://github.com/vidcapbench/vidcapbench
공식 코드VidCapBench: A Comprehensive Benchmark of Video Captioning for Controllable Text-to-Video Generation
https://github.com/vision-cair/chatcaptioner
공식 코드Video ChatCaptioner: Towards Enriched Spatiotemporal Descriptions
https://github.com/vsislab/Controllable_XGating
공식 코드Controllable Video Captioning with POS Sequence Guidance Based on Gated Fusion Network
https://github.com/wangjk666/omnivid
공식 코드OmniVid: A Generative Framework for Universal Video Understanding
https://github.com/wangyu-ustc/lvchat
공식 코드https://github.com/whb139426/grounded-video-llm
공식 코드Grounded-VideoLLM: Sharpening Fine-grained Temporal Grounding in Video Large Language Models
https://github.com/whwu95/Cap4Video
공식 코드Cap4Video: What Can Auxiliary Captions Do for Text-Video Retrieval?
https://github.com/willyfh/msvd-indonesian
공식 코드MSVD-Indonesian: A Benchmark for Multimodal Video-Text Tasks in Indonesian
https://github.com/wpr001/ugc_videocaptioner
공식 코드UGC-VideoCaptioner: An Omni UGC Video Detail Caption Model and New Benchmarks
https://github.com/x-plug/youku-mplug
공식 코드Youku-mPLUG: A 10 Million Large-scale Chinese Video-Language Dataset for Pre-training and Benchmarks
https://github.com/xid32/naacl_2025_twm
공식 코드Temporal Working Memory: Query-Guided Segment Refinement for Enhanced Multimodal Understanding
https://github.com/xumengyaamy/swinmlp_trancap
공식 코드Rethinking Surgical Captioning: End-to-End Window-Based MLP Transformer Using Patches
https://github.com/yangbang18/CLIP-Captioner
공식 코드CLIP Meets Video Captioning: Concept-Aware Representation Learning Does Matter
https://github.com/yangbang18/Non-Autoregressive-Video-Captioning
공식 코드https://github.com/yangbang18/multicapclip
공식 코드MultiCapCLIP: Auto-Encoding Prompts for Zero-Shot Multilingual Visual Captioning
https://github.com/yangbang18/zeronlg
공식 코드https://github.com/yaolinli/vce
공식 코드Edit As You Wish: Video Caption Editing with Multi-grained User Control
https://github.com/yehli/xmodaler
공식 코드X-modaler: A Versatile and High-performance Codebase for Cross-modal Analytics
https://github.com/yellow-binary-tree/mmduet
공식 코드https://github.com/ylqi/gl-rg
공식 코드GL-RG: Global-Local Representation Granularity for Video Captioning
https://github.com/yoadtew/zero-shot-video-to-text
공식 코드https://github.com/yuezih/movie101
공식 코드https://github.com/yuhui-zh15/c3
공식 코드Connect, Collapse, Corrupt: Learning Cross-Modal Tasks with Uni-Modal Data
https://github.com/yunlong10/CAT-V
공식 코드https://github.com/yuxuan-w/geb-plus
공식 코드GEB+: A Benchmark for Generic Event Boundary Captioning, Grounding and Retrieval
https://github.com/yytzsy/smcg
공식 코드https://github.com/yytzsy/syntax-customized-video-captioning
공식 코드Syntax Customized Video Captioning by Imitating Exemplar Sentences
https://github.com/zchoi/vcrn
공식 코드Visual Commonsense-aware Representation Network for Video Captioning
https://github.com/zihuaevan/flashsd
공식 코드FLASH: Latent-Aware Semi-Autoregressive Speculative Decoding for Multimodal Tasks
https://github.com/zjr2000/context-gebc
공식 코드Exploiting Context Information for Generic Event Boundary Captioning
https://github.com/zjr2000/gvl
공식 코드Learning Grounded Vision-Language Representation for Versatile Understanding in Untrimmed Videos
https://github.com/zjr2000/llmva-gebc
공식 코드LLMVA-GEBC: Large Language Model with Video Adapter for Generic Event Boundary Captioning
https://github.com/zohrehghaderi/vasta
공식 코드Diverse Video Captioning by Adaptive Spatio-temporal Attention
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.