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Visual Question Answering (VQA)
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Visual Question Answering (VQA) is a task in computer vision that involves answering questions about an image. The goal of VQA is to teach machines to understand the content of an image and answer questions about it in natural language.
Image Source: visualqa.org
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/internlm/internlm-xcomposer
공식 코드https://github.com/invictus717/MiCo
공식 코드https://github.com/ir-nlp-csui/indonli
공식 코드IndoNLI: A Natural Language Inference Dataset for Indonesian
https://github.com/isaaccorley/chesapeakersc
공식 코드https://github.com/ishmamt/Visual-Robustness
공식 코드Visual Robustness Benchmark for Visual Question Answering (VQA)
https://github.com/itaigat/perceptual-score
공식 코드Perceptual Score: What Data Modalities Does Your Model Perceive?
https://github.com/itaigat/removing-bias-in-multi-modal-classifiers
공식 코드Removing Bias in Multi-modal Classifiers: Regularization by Maximizing Functional Entropies
https://github.com/itemzheng/kddaug
공식 코드Rethinking Data Augmentation for Robust Visual Question Answering
https://github.com/ivan-tang-3d/enel
공식 코드Exploring the Potential of Encoder-free Architectures in 3D LMMs
https://github.com/ivy-dl/memory
공식 코드https://github.com/j-min/VL-T5
공식 코드https://github.com/jackroos/VL-BERT
공식 코드VL-BERT: Pre-training of Generic Visual-Linguistic Representations
https://github.com/jadecxliu/codeqa
공식 코드CodeQA: A Question Answering Dataset for Source Code Comprehension
https://github.com/janhq/visual-thinker
공식 코드AlphaMaze: Enhancing Large Language Models' Spatial Intelligence via GRPO
https://github.com/jayleicn/ClipBERT
공식 코드Less is More: ClipBERT for Video-and-Language Learning via Sparse Sampling
https://github.com/jd-ai-research-nlp/evidr
공식 코드EviDR: Evidence-Emphasized Discrete Reasoning for Reasoning Machine Reading Comprehension
https://github.com/jd-ai-research-nlp/ror
공식 코드RoR: Read-over-Read for Long Document Machine Reading Comprehension
https://github.com/jefferyzhan/griffon
공식 코드Griffon-G: Bridging Vision-Language and Vision-Centric Tasks via Large Multimodal Models
https://github.com/jialinwu17/mavex
공식 코드https://github.com/jiangsongtao/med-moe
공식 코드Med-MoE: Mixture of Domain-Specific Experts for Lightweight Medical Vision-Language Models
https://github.com/jiangsongtao/tinymed
공식 코드Med-MoE: Mixture of Domain-Specific Experts for Lightweight Medical Vision-Language Models
https://github.com/jiangsongtao/vtprompt
공식 코드https://github.com/jiaqi5598/adavib
공식 코드https://github.com/jiasenlu/HieCoAttenVQA
공식 코드Hierarchical Question-Image Co-Attention for Visual Question Answering
https://github.com/jiayuww/SpatialEval
공식 코드Is A Picture Worth A Thousand Words? Delving Into Spatial Reasoning for Vision Language Models
https://github.com/jiazhen-code/PhD
공식 코드PhD: A ChatGPT-Prompted Visual hallucination Evaluation Dataset
https://github.com/jieyuz2/provision
공식 코드ProVision: Programmatically Scaling Vision-centric Instruction Data for Multimodal Language Models
https://github.com/jihaonew/mm-instruct
공식 코드MM-Instruct: Generated Visual Instructions for Large Multimodal Model Alignment
https://github.com/jihoonlee9898/rvcd
공식 코드https://github.com/jihuishan/flexible_evaluation_for_vqa_mm24
공식 코드Towards Flexible Evaluation for Generative Visual Question Answering
https://github.com/jind11/MMM-MCQA
공식 코드MMM: Multi-stage Multi-task Learning for Multi-choice Reading Comprehension
https://github.com/jingjing12110/mixphm
공식 코드MixPHM: Redundancy-Aware Parameter-Efficient Tuning for Low-Resource Visual Question Answering
https://github.com/jingjing12110/x-ggm
공식 코드X-GGM: Graph Generative Modeling for Out-of-Distribution Generalization in Visual Question Answering
https://github.com/jingkang50/funqa
공식 코드https://github.com/jingyangqiao/coin
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.