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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/microsoft/unilm
공식 코드LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding
https://github.com/microsoft/unilm/tree/master/beit
공식 코드Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language Tasks
https://github.com/microsoft/unilm/tree/master/kosmos-2
공식 코드Kosmos-2: Grounding Multimodal Large Language Models to the World
https://github.com/microsoft/unilm/tree/master/layoutlmv3
공식 코드LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking
https://github.com/mightyzau/infmllm
공식 코드https://github.com/mihirp1998/Disentangling-3D-Prototypical-Nets
공식 코드Disentangling 3D Prototypical Networks For Few-Shot Concept Learning
https://github.com/milvlg/bst
공식 코드Bilaterally Slimmable Transformer for Elastic and Efficient Visual Question Answering
https://github.com/milvlg/imp
공식 코드Imp: Highly Capable Large Multimodal Models for Mobile Devices
https://github.com/milvlg/prophet
공식 코드https://github.com/mineanybuild/mineanybuild
공식 코드MineAnyBuild: Benchmarking Spatial Planning for Open-world AI Agents
https://github.com/minhquan6203/vitextvqa-dataset
공식 코드https://github.com/mintelligence-group/cmfeed
공식 코드Synthesizing Sentiment-Controlled Feedback For Multimodal Text and Image Data
https://github.com/mirthai/med3dvlm
공식 코드Med3DVLM: An Efficient Vision-Language Model for 3D Medical Image Analysis
https://github.com/mit-han-lab/llm-awq
공식 코드https://github.com/mizuumi/JDocQA
공식 코드JDocQA: Japanese Document Question Answering Dataset for Generative Language Models
https://github.com/ml-research/ILLUME
공식 코드ILLUME: Rationalizing Vision-Language Models through Human Interactions
https://github.com/ml-research/slash
공식 코드https://github.com/mlfoundations/open_flamingo
공식 코드OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models
https://github.com/mlpc-ucsd/bliva
공식 코드BLIVA: A Simple Multimodal LLM for Better Handling of Text-Rich Visual Questions
https://github.com/mmdocrag/mmdocrag
공식 코드Benchmarking Retrieval-Augmented Multimomal Generation for Document Question Answering
https://github.com/mneuronico/griw-world-spatial-orientation-task
공식 코드From Text to Space: Mapping Abstract Spatial Models in LLMs during a Grid-World Navigation Task
https://github.com/mobarakol/Surgical_SceneGraph_Generation
공식 코드Learning and Reasoning with the Graph Structure Representation in Robotic Surgery
https://github.com/mobarakol/pitvqa
공식 코드PitVQA: Image-grounded Text Embedding LLM for Visual Question Answering in Pituitary Surgery
https://github.com/modelscope/data-juicer
공식 코드DetailMaster: Can Your Text-to-Image Model Handle Long Prompts?
https://github.com/modelscope/ms-swift
공식 코드https://github.com/modelscope/swift
공식 코드https://github.com/mohamedaghzal/llms-as-path-planners
공식 코드https://github.com/moramisu/qft
공식 코드Design as Desired: Utilizing Visual Question Answering for Multimodal Pre-training
https://github.com/moramisu/qft-miccai2024
공식 코드Design as Desired: Utilizing Visual Question Answering for Multimodal Pre-training
https://github.com/morty-xu/tcc-bench
공식 코드TCC-Bench: Benchmarking the Traditional Chinese Culture Understanding Capabilities of MLLMs
https://github.com/mpsc-umbc/efficient-vision-language-models-a-survey
공식 코드https://github.com/mshahabsepehri/mediconfusion
공식 코드https://github.com/mshukor/ima-lmms
공식 코드https://github.com/msiam/pixfoundation
공식 코드PixFoundation: Are We Heading in the Right Direction with Pixel-level Vision Foundation Models?
https://github.com/msiip/medm-vl
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.