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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/stogiannidis/srbench
공식 코드Mind the Gap: Benchmarking Spatial Reasoning in Vision-Language Models
https://github.com/subho406/OmniNet
공식 코드OmniNet: A unified architecture for multi-modal multi-task learning
https://github.com/sunanhe/meddr
공식 코드GSCo: Towards Generalizable AI in Medicine via Generalist-Specialist Collaboration
https://github.com/sungyeonparkk/nuplanqa
공식 코드https://github.com/superagi/veagle
공식 코드https://github.com/svaishnavi411/multimodal-recurrence
공식 코드Multimodal fusion of imaging and genomics for lung cancer recurrence prediction
https://github.com/swadesh06/revisting_cropa
공식 코드https://github.com/swarupbehera/mAVQA
공식 코드https://github.com/swordlidev/evaluation-multimodal-llms-survey
공식 코드https://github.com/syon-li/depwignn
공식 코드DepWiGNN: A Depth-wise Graph Neural Network for Multi-hop Spatial Reasoning in Text
https://github.com/szhang42/Contextual_dropout_release
공식 코드Contextual Dropout: An Efficient Sample-Dependent Dropout Module
https://github.com/szhang42/alignment_attention
공식 코드https://github.com/szzexpoi/AiR
공식 코드https://github.com/szzexpoi/rex
공식 코드https://github.com/taco-group/autotrust
공식 코드AutoTrust: Benchmarking Trustworthiness in Large Vision Language Models for Autonomous Driving
https://github.com/taco-group/re-align
공식 코드Re-Align: Aligning Vision Language Models via Retrieval-Augmented Direct Preference Optimization
https://github.com/tae2hee/coqah
공식 코드https://github.com/tanchongmin/arc-challenge
공식 코드An Approach to Solving the Abstraction and Reasoning Corpus (ARC) Challenge
https://github.com/tanjatang/DMVCR
공식 코드Cognitive Visual Commonsense Reasoning Using Dynamic Working Memory
https://github.com/taokz/biomedgpt
공식 코드BiomedGPT: A Generalist Vision-Language Foundation Model for Diverse Biomedical Tasks
https://github.com/tejasG53/vqa_mutant
공식 코드MUTANT: A Training Paradigm for Out-of-Distribution Generalization in Visual Question Answering
https://github.com/tencentarc/gvt
공식 코드What Makes for Good Visual Tokenizers for Large Language Models?
https://github.com/text-to-cadquery/text-to-cadquery
공식 코드Text-to-CadQuery: A New Paradigm for CAD Generation with Scalable Large Model Capabilities
https://github.com/tgc1997/RMN
공식 코드Learning to Discretely Compose Reasoning Module Networks for Video Captioning
https://github.com/thisisus7/ing-vp
공식 코드https://github.com/thomaswei-cn/MC-CoT
공식 코드https://github.com/thudm/cogvlm
공식 코드https://github.com/thudm/cogvlm2
공식 코드CogVLM2: Visual Language Models for Image and Video Understanding
https://github.com/thudm/glm-4
공식 코드CogVLM2: Visual Language Models for Image and Video Understanding
https://github.com/thunlp-mt/cospace
공식 코드CoSpace: Benchmarking Continuous Space Perception Ability for Vision-Language Models
https://github.com/thunlp-mt/fiig
공식 코드https://github.com/tianyu-z/vcr
공식 코드VCR: A Task for Pixel-Level Complex Reasoning in Vision Language Models via Restoring Occluded Text
https://github.com/tibhannover/patent-figure-classification
공식 코드Patent Figure Classification using Large Vision-language Models
https://github.com/timmy-chan/miss
공식 코드MISS: A Generative Pretraining and Finetuning Approach for Med-VQA
https://github.com/tjvsonsbeek/open-ended-medical-vqa
공식 코드Open-Ended Medical Visual Question Answering Through Prefix Tuning of Language Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.