Papers with Code 아카이브

Visual Question Answering

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

5

평가표

30

보존된 평가 기록

334

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/internlm/internlm-xcomposer

공식 코드

InternLM-XComposer2: Mastering Free-form Text-Image Composition and Comprehension in Vision-Language Large Model

https://github.com/invictus717/MiCo

공식 코드

Explore the Limits of Omni-modal Pretraining at Scale

https://github.com/ir-nlp-csui/indonli

공식 코드

IndoNLI: A Natural Language Inference Dataset for Indonesian

https://github.com/isaaccorley/chesapeakersc

공식 코드

Seeing the roads through the trees: A benchmark for modeling spatial dependencies with aerial imagery

https://github.com/ishmamt/Visual-Robustness

공식 코드

Visual Robustness Benchmark for Visual Question Answering (VQA)

https://github.com/itaigat/perceptual-score

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Perceptual Score: What Data Modalities Does Your Model Perceive?

https://github.com/itaigat/removing-bias-in-multi-modal-classifiers

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Removing Bias in Multi-modal Classifiers: Regularization by Maximizing Functional Entropies

https://github.com/itemzheng/kddaug

공식 코드

Rethinking Data Augmentation for Robust Visual Question Answering

https://github.com/ivy-dl/memory

공식 코드

End-to-End Egospheric Spatial Memory

https://github.com/j-min/VL-T5

공식 코드

Unifying Vision-and-Language Tasks via Text Generation

https://github.com/jackroos/VL-BERT

공식 코드

VL-BERT: Pre-training of Generic Visual-Linguistic Representations

https://github.com/janhq/visual-thinker

공식 코드

AlphaMaze: Enhancing Large Language Models' Spatial Intelligence via GRPO

https://github.com/jefferyzhan/griffon

공식 코드

Griffon-G: Bridging Vision-Language and Vision-Centric Tasks via Large Multimodal Models

https://github.com/jialinwu17/mavex

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Multi-Modal Answer Validation for Knowledge-Based VQA

https://github.com/jiangsongtao/vtprompt

공식 코드

Joint Visual and Text Prompting for Improved Object-Centric Perception with Multimodal Large Language Models

https://github.com/jiaqi5598/adavib

공식 코드

Mitigating Hallucinations in Large Vision-Language Models by Adaptively Constraining Information Flow

https://github.com/jiasenlu/HieCoAttenVQA

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Hierarchical Question-Image Co-Attention for Visual Question Answering

https://github.com/jiayuww/SpatialEval

공식 코드

Is A Picture Worth A Thousand Words? Delving Into Spatial Reasoning for Vision Language Models

https://github.com/jieyuz2/provision

공식 코드

ProVision: Programmatically Scaling Vision-centric Instruction Data for Multimodal Language Models

https://github.com/jihaonew/mm-instruct

공식 코드

MM-Instruct: Generated Visual Instructions for Large Multimodal Model Alignment

https://github.com/jihoonlee9898/rvcd

공식 코드

Retrieval Visual Contrastive Decoding to Mitigate Object Hallucinations in Large Vision-Language Models

https://github.com/jihuishan/flexible_evaluation_for_vqa_mm24

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Towards Flexible Evaluation for Generative Visual Question Answering

https://github.com/jingjing12110/mixphm

공식 코드

MixPHM: Redundancy-Aware Parameter-Efficient Tuning for Low-Resource Visual Question Answering

https://github.com/jingjing12110/x-ggm

공식 코드

X-GGM: Graph Generative Modeling for Out-of-Distribution Generalization in Visual Question Answering

https://github.com/jingyangqiao/coin

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Large Continual Instruction Assistant

https://github.com/jinhaoduan/truthprint

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TruthPrInt: Mitigating LVLM Object Hallucination Via Latent Truthful-Guided Pre-Intervention

https://github.com/jinlhe/pefomed

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PeFoMed: Parameter Efficient Fine-tuning of Multimodal Large Language Models for Medical Imaging

https://github.com/jmerullo/limber

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Linearly Mapping from Image to Text Space

https://github.com/jmhb0/microvqa

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MicroVQA: A Multimodal Reasoning Benchmark for Microscopy-Based Scientific Research

https://github.com/jnhwkim/MulLowBiVQA

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Hadamard Product for Low-rank Bilinear Pooling

https://github.com/jnhwkim/ban-vqa

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Bilinear Attention Networks

https://github.com/jnhwkim/nips-mrn-vqa

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Multimodal Residual Learning for Visual QA

https://github.com/joey1993/ad-themes

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Recommending Themes for Ad Creative Design via Visual-Linguistic Representations

https://github.com/johncaged/OPT_Questioner

공식 코드

Enhancing Vision-Language Pre-Training with Jointly Learned Questioner and Dense Captioner

https://github.com/jokieleung/LPF-VQA

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LPF: A Language-Prior Feedback Objective Function for De-biased Visual Question Answering

https://github.com/joshuafeinglass/vl-detector-eval

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Towards Addressing the Misalignment of Object Proposal Evaluation for Vision-Language Tasks via Semantic Grounding

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