https://github.com/IAAR-Shanghai/FTIIBench
공식 코드FTII-Bench: A Comprehensive Multimodal Benchmark for Flow Text with Image Insertion
Papers with Code 아카이브
Image Comprehension
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/berkeley-hipie/hipie
공식 코드https://github.com/dlyuangod/artgpt-4
공식 코드ArtGPT-4: Towards Artistic-understanding Large Vision-Language Models with Enhanced Adapter
https://github.com/dvlab-research/minigemini
공식 코드Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models
https://github.com/internlm/internlm-xcomposer
공식 코드https://github.com/kge-sun/mm-math
공식 코드https://github.com/lerogo/mmgenbench
공식 코드MMGenBench: Evaluating the Limits of LMMs from the Text-to-Image Generation Perspective
https://github.com/linziyang1999/vga-visual-gui-assistant
공식 코드VGA: Vision GUI Assistant -- Minimizing Hallucinations through Image-Centric Fine-Tuning
https://github.com/liujunzhuo/FineCops-Ref
공식 코드https://github.com/mightyzau/regionblip
공식 코드RegionBLIP: A Unified Multi-modal Pre-training Framework for Holistic and Regional Comprehension
https://github.com/tencentarc/divot
공식 코드Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation
https://github.com/thunlp-mt/streamingbench
공식 코드StreamingBench: Assessing the Gap for MLLMs to Achieve Streaming Video Understanding
https://github.com/umd-huang-lab/sima
공식 코드Enhancing Visual-Language Modality Alignment in Large Vision Language Models via Self-Improvement
https://github.com/wivizhang/earthgpt
공식 코드https://github.com/xauat-liushipeng/clic
공식 코드CLIC: Contrastive Learning Framework for Unsupervised Image Complexity Representation
https://github.com/xuliu-cyber/rsunivlm
공식 코드https://github.com/yeongjoonju/mire
공식 코드https://github.com/yihedeng9/stic
공식 코드Enhancing Large Vision Language Models with Self-Training on Image Comprehension
https://github.com/MS-P3/code3/tree/main/internlm
https://github.com/MindSpore-scientific-2/code-14/tree/main/internlm
https://github.com/dvlab-research/MGM
Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models
https://github.com/hasanar1f/llava-hallunication-fix
Paying More Attention to Image: A Training-Free Method for Alleviating Hallucination in LVLMs
https://github.com/opengvlab/phygenbench
MMIU: Multimodal Multi-image Understanding for Evaluating Large Vision-Language Models
https://github.com/shihaozhaozsh/lavi-bridge
https://github.com/vista-h/gpt-4v_social_media
CoCoT: Contrastive Chain-of-Thought Prompting for Large Multimodal Models with Multiple Image Inputs
https://github.com/yiyangzhou/csr
Enhancing Visual-Language Modality Alignment in Large Vision Language Models via Self-Improvement
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.