https://github.com/idt-iti/vidctx
공식 코드VidCtx: Context-aware Video Question Answering with Image Models
Papers with Code 아카이브
Video Question Answering
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/imagegridworth/IG-VLM
공식 코드An Image Grid Can Be Worth a Video: Zero-shot Video Question Answering Using a VLM
https://github.com/intellabs/multimodal_cognitive_ai
공식 코드Analyzing Zero-Shot Abilities of Vision-Language Models on Video Understanding Tasks
https://github.com/internlm/internlm-xcomposer
공식 코드https://github.com/jayleicn/ClipBERT
공식 코드Less is More: ClipBERT for Video-and-Language Learning via Sparse Sampling
https://github.com/jayleicn/TVQA-PLUS
공식 코드TVQA+: Spatio-Temporal Grounding for Video Question Answering
https://github.com/jayleicn/TVQAplus
공식 코드TVQA+: Spatio-Temporal Grounding for Video Question Answering
https://github.com/jayleicn/singularity
공식 코드https://github.com/jerryyli/svitt
공식 코드https://github.com/jh-yi/video-panda
공식 코드Video-Panda: Parameter-efficient Alignment for Encoder-free Video-Language Models
https://github.com/jieyuz2/taskmeanything
공식 코드https://github.com/jingkang50/funqa
공식 코드https://github.com/joe-rabbit/videoqa_pilot_study
공식 코드https://github.com/joez17/videoniah
공식 코드Needle In A Video Haystack: A Scalable Synthetic Evaluator for Video MLLMs
https://github.com/jongwoopark7978/LVNet
공식 코드Too Many Frames, Not All Useful: Efficient Strategies for Long-Form Video QA
https://github.com/jpthu17/HBI
공식 코드Video-Text as Game Players: Hierarchical Banzhaf Interaction for Cross-Modal Representation Learning
https://github.com/jpthu17/emcl
공식 코드Expectation-Maximization Contrastive Learning for Compact Video-and-Language Representations
https://github.com/jssprz/video_captioning_datasets
공식 코드https://github.com/kahnchana/mvu
공식 코드https://github.com/kkahatapitiya/langrepo
공식 코드https://github.com/klauscc/dam
공식 코드DAM: Dynamic Adapter Merging for Continual Video QA Learning
https://github.com/klauscc/vindlu
공식 코드VindLU: A Recipe for Effective Video-and-Language Pretraining
https://github.com/kpeng9510/ravar
공식 코드https://github.com/lavi-lab/tg-vid
공식 코드https://github.com/leeyn-43/clover
공식 코드Clover: Towards A Unified Video-Language Alignment and Fusion Model
https://github.com/leon1207/video-rag-master
공식 코드Video-RAG: Visually-aligned Retrieval-Augmented Long Video Comprehension
https://github.com/lgresearch/ytcommentqa
공식 코드YTCommentQA: Video Question Answerability in Instructional Videos
https://github.com/linjieli222/HERO
공식 코드HERO: Hierarchical Encoder for Video+Language Omni-representation Pre-training
https://github.com/longmalongma/desa2va
공식 코드Decoupled Seg Tokens Make Stronger Reasoning Video Segmenter and Grounder
https://github.com/longvideobench/longvideobench
공식 코드LongVideoBench: A Benchmark for Long-context Interleaved Video-Language Understanding
https://github.com/lunaproject22/causalchaos
공식 코드https://github.com/magic-research/PLLaVA
공식 코드PLLaVA : Parameter-free LLaVA Extension from Images to Videos for Video Dense Captioning
https://github.com/maitreyapatel/cripp-vqa
공식 코드CRIPP-VQA: Counterfactual Reasoning about Implicit Physical Properties via Video Question Answering
https://github.com/mayhugotong/videoinsta
공식 코드VideoINSTA: Zero-shot Long Video Understanding via Informative Spatial-Temporal Reasoning with LLMs
https://github.com/mbzuai-oryx/video-chatgpt
공식 코드Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models
https://github.com/mbzuai-oryx/videogpt-plus
공식 코드VideoGPT+: Integrating Image and Video Encoders for Enhanced Video Understanding
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.