https://github.com/riddickzhou/llm-graph-synthetic-reasoning
공식 코드Enhancing Logical Reasoning in Large Language Models through Graph-based Synthetic Data
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Visual Question Answering
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/rikeilong/bay-cat
공식 코드CAT: Enhancing Multimodal Large Language Model to Answer Questions in Dynamic Audio-Visual Scenarios
https://github.com/rikeilong/mcd-foravqa
공식 코드Answering Diverse Questions via Text Attached with Key Audio-Visual Clues
https://github.com/rlhf-v/rlaif-v
공식 코드RLAIF-V: Open-Source AI Feedback Leads to Super GPT-4V Trustworthiness
https://github.com/rlhf-v/rlhf-v
공식 코드https://github.com/robotics-upo/og-sgg
공식 코드https://github.com/rohit901/VANE-Bench
공식 코드VANE-Bench: Video Anomaly Evaluation Benchmark for Conversational LMMs
https://github.com/rshnk73/pistaq-sreqa
공식 코드Disentangling Extraction and Reasoning in Multi-hop Spatial Reasoning
https://github.com/rubenpt91/MP-DocVQA-Framework
공식 코드https://github.com/rucaibox/lamoc
공식 코드Zero-shot Visual Question Answering with Language Model Feedback
https://github.com/rucaibox/pope
공식 코드Evaluating Object Hallucination in Large Vision-Language Models
https://github.com/russellyq/medhalltune
공식 코드https://github.com/s3vqa/s3vqa.github.io
공식 코드Select, Substitute, Search: A New Benchmark for Knowledge-Augmented Visual Question Answering
https://github.com/saccharomycetes/mllms_know
공식 코드MLLMs Know Where to Look: Training-free Perception of Small Visual Details with Multimodal LLMs
https://github.com/saccharomycetes/visual_crop_zsvqa
공식 코드Towards Perceiving Small Visual Details in Zero-shot Visual Question Answering with Multimodal LLMs
https://github.com/salanueva/CBM
공식 코드Image Captioning for Effective Use of Language Models in Knowledge-Based Visual Question Answering
https://github.com/salesforce/BiST
공식 코드BiST: Bi-directional Spatio-Temporal Reasoning for Video-Grounded Dialogues
https://github.com/salesforce/lavis
공식 코드https://github.com/salesforceairesearch/taco
공식 코드TACO: Learning Multi-modal Action Models with Synthetic Chains-of-Thought-and-Action
https://github.com/samarth4149/scramble
공식 코드Enhancing Compositional Reasoning in Vision-Language Models with Synthetic Preference Data
https://github.com/sameerdharur/sorting-vqa
공식 코드SOrT-ing VQA Models : Contrastive Gradient Learning for Improved Consistency
https://github.com/sanjayss34/codevqa
공식 코드https://github.com/sayazhang/spatial-query-by-sketch
공식 코드DeepSSN: a deep convolutional neural network to assess spatial scene similarity
https://github.com/sberbank-ai/fusion_brain_aij2021
공식 코드https://github.com/schuture/deeptumorvqa
공식 코드https://github.com/sdc17/crossget
공식 코드CrossGET: Cross-Guided Ensemble of Tokens for Accelerating Vision-Language Transformers
https://github.com/sdesabbata/geospatial-mechanistic-interpretability
공식 코드Geospatial Mechanistic Interpretability of Large Language Models
https://github.com/sdlzy/arc
공식 코드Joint Answering and Explanation for Visual Commonsense Reasoning
https://github.com/sergiotasconmorales/consistency_vqa
공식 코드Consistency-preserving Visual Question Answering in Medical Imaging
https://github.com/sergiotasconmorales/imp_vqa
공식 코드Logical Implications for Visual Question Answering Consistency
https://github.com/sergiotasconmorales/locvqa
공식 코드https://github.com/sergiotasconmorales/locvqallm
공식 코드Targeted Visual Prompting for Medical Visual Question Answering
https://github.com/servicenow/fm2ds
공식 코드https://github.com/shahin-01/vqa-ad
공식 코드Explaining Autonomous Driving Actions with Visual Question Answering
https://github.com/shailaja183/clevr_hyp
공식 코드https://github.com/shailaja183/objectconceptlearning
공식 코드Help Me Identify: Is an LLM+VQA System All We Need to Identify Visual Concepts?
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.