https://github.com/boreng0817/ifcap
공식 코드IFCap: Image-like Retrieval and Frequency-based Entity Filtering for Zero-shot Captioning
Papers with Code 아카이브
Video Captioning
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- 하위 태스크
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- 평가표
- 13
- 보존된 평가 기록
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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/bytedance/Shot2Story
공식 코드Shot2Story20K: A New Benchmark for Comprehensive Understanding of Multi-shot Videos
https://github.com/bytedance/tarsier
공식 코드https://github.com/bytedance/video-salmonn-2
공식 코드video-SALMONN 2: Captioning-Enhanced Audio-Visual Large Language Models
https://github.com/camma-public/peskavlp
공식 코드Learning Multi-modal Representations by Watching Hundreds of Surgical Video Lectures
https://github.com/camma-public/surgvlp
공식 코드Learning Multi-modal Representations by Watching Hundreds of Surgical Video Lectures
https://github.com/captanlevi/Meaning-guided-video-captioning-
공식 코드https://github.com/chihyaoma/cyclical-visual-captioning
공식 코드Learning to Generate Grounded Visual Captions without Localization Supervision
https://github.com/chunhuizng/mllm-video-captioner
공식 코드https://github.com/computer-vision-in-the-wild/cvinw_readings
공식 코드Vision-Language Pre-training: Basics, Recent Advances, and Future Trends
https://github.com/csjihwanh/soccernet-mlv
공식 코드https://github.com/d-rothen/bmhrl
공식 코드https://github.com/damo-nlp-sg/videollama2
공식 코드VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMs
https://github.com/dot27/temporal-tessellation
공식 코드Temporal Tessellation: A Unified Approach for Video Analysis
https://github.com/frankxu2004/cooking-procedural-extraction
공식 코드A Benchmark for Structured Procedural Knowledge Extraction from Cooking Videos
https://github.com/fundamentalvision/Uni-Perceiver
공식 코드Uni-Perceiver-MoE: Learning Sparse Generalist Models with Conditional MoEs
https://github.com/gingsi/coot-videotext
공식 코드COOT: Cooperative Hierarchical Transformer for Video-Text Representation Learning
https://github.com/google-research-datasets/Video-Timeline-Tags-ViTT
공식 코드https://github.com/google-research/scenic
공식 코드https://github.com/google-research/scenic/tree/main/scenic/projects/vid2seq
공식 코드Vid2Seq: Large-Scale Pretraining of a Visual Language Model for Dense Video Captioning
https://github.com/gramuah/lvc
공식 코드https://github.com/gx77/dual-stream-transformer-for-generic-event-boundary-captioning
공식 코드Dual-Stream Transformer for Generic Event Boundary Captioning
https://github.com/gyxxyg/vtg-llm
공식 코드VTG-LLM: Integrating Timestamp Knowledge into Video LLMs for Enhanced Video Temporal Grounding
https://github.com/haiyang-w/git
공식 코드GiT: Towards Generalist Vision Transformer through Universal Language Interface
https://github.com/hassanhub/R3Transformer
공식 코드https://github.com/hayyubi/views
공식 코드https://github.com/hcplab-sysu/causal-vlreasoning
공식 코드CausalVLR: A Toolbox and Benchmark for Visual-Linguistic Causal Reasoning
https://github.com/hcplab-sysu/causalvlr
공식 코드CausalVLR: A Toolbox and Benchmark for Visual-Linguistic Causal Reasoning
https://github.com/henryhzy/vl-pet
공식 코드VL-PET: Vision-and-Language Parameter-Efficient Tuning via Granularity Control
https://github.com/hobincar/SGN
공식 코드https://github.com/holistic-video-understanding/HVU-Dataset
공식 코드https://github.com/huangb23/vtimellm
공식 코드https://github.com/hughplay/vtt
공식 코드https://github.com/hwang1996/Meta-Concepts-for-Video-Captioning
공식 코드https://github.com/intellabs/multimodal_cognitive_ai
공식 코드Analyzing Zero-Shot Abilities of Vision-Language Models on Video Understanding Tasks
https://github.com/j-min/HiREST
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.