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Visual Question Answering

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Papers

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Showing 1151–1200 of 2177 papers

TitleStatusHype
Enhancing Generalization in Medical Visual Question Answering Tasks via Gradient-Guided Model Perturbation—0
ViLMedic: a framework for research at the intersection of vision and language in medical AI—0
Enhancing Explainability in Multimodal Large Language Models Using Ontological Context—0
Enhancing Document Information Analysis with Multi-Task Pre-training: A Robust Approach for Information Extraction in Visually-Rich Documents—0
MIMOQA: Multimodal Input Multimodal Output Question Answering—0
MindBench: A Comprehensive Benchmark for Mind Map Structure Recognition and Analysis—0
Mindstorms in Natural Language-Based Societies of Mind—0
Enhancing BERT-Based Visual Question Answering through Keyword-Driven Sentence Selection—0
Enhanced Textual Feature Extraction for Visual Question Answering: A Simple Convolutional Approach—0
Enhanced Multimodal RAG-LLM for Accurate Visual Question Answering—0
Enforcing Reasoning in Visual Commonsense Reasoning—0
End-to-End Vision Tokenizer Tuning—0
Encyclopedic VQA: Visual questions about detailed properties of fine-grained categories—0
Enabling Multimodal Generation on CLIP via Vision-Language Knowledge Distillation—0
Where is this coming from? Making groundedness count in the evaluation of Document VQA models—0
Enabling Multimodal Generation on CLIP via Vision-Language Knowledge Distillation—0
Mitigating Hallucination in Visual-Language Models via Re-Balancing Contrastive Decoding—0
EmoAssist: Emotional Assistant for Visual Impairment Community—0
Mitigating Low-Level Visual Hallucinations Requires Self-Awareness: Database, Model and Training Strategy—0
Data-augmented phrase-level alignment for mitigating object hallucination—0
Mitigating the Impact of Attribute Editing on Face Recognition—0
MIVC: Multiple Instance Visual Component for Visual-Language Models—0
Emerging Pixel Grounding in Large Multimodal Models Without Grounding Supervision—0
Embodied Scene Understanding for Vision Language Models via MetaVQA—0
Mixture of Rationale: Multi-Modal Reasoning Mixture for Visual Question Answering—0
Analysis of Visual Question Answering Algorithms with attention model—0
EmbodiedGPT: Vision-Language Pre-Training via Embodied Chain of Thought—0
MM1.5: Methods, Analysis & Insights from Multimodal LLM Fine-tuning—0
MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training—0
MMAR: Towards Lossless Multi-Modal Auto-Regressive Probabilistic Modeling—0
ELIXR: Towards a general purpose X-ray artificial intelligence system through alignment of large language models and radiology vision encoders—0
Eliminating the Language Bias for Visual Question Answering with fine-grained Causal Intervention—0
MMCOMPOSITION: Revisiting the Compositionality of Pre-trained Vision-Language Models—0
MMCTAgent: Multi-modal Critical Thinking Agent Framework for Complex Visual Reasoning—0
Eliminating Catastrophic Interference with Biased Competition—0
MMED: A Multi-domain and Multi-modality Event Dataset—0
MME-Finance: A Multimodal Finance Benchmark for Expert-level Understanding and Reasoning—0
ElectroVizQA: How well do Multi-modal LLMs perform in Electronics Visual Question Answering?—0
Efficient Multi-modal Large Language Models via Visual Token Grouping—0
EfficientLLaVA: Generalizable Auto-Pruning for Large Vision-language Models—0
EfficientLLaVA:Generalizable Auto-Pruning for Large Vision-language Models—0
MMIU: Dataset for Visual Intent Understanding in Multimodal Assistants—0
MMKB-RAG: A Multi-Modal Knowledge-Based Retrieval-Augmented Generation Framework—0
Efficient Few-Shot Continual Learning in Vision-Language Models—0
Where To Look: Focus Regions for Visual Question Answering—0
AMXFP4: Taming Activation Outliers with Asymmetric Microscaling Floating-Point for 4-bit LLM Inference—0
MM-R^3: On (In-)Consistency of Multi-modal Large Language Models (MLLMs)—0
Efficient Bilinear Attention-based Fusion for Medical Visual Question Answering—0
MM-SpuBench: Towards Better Understanding of Spurious Biases in Multimodal LLMs—0
Efficiency in Focus: LayerNorm as a Catalyst for Fine-tuning Medical Visual Language Pre-trained Models—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MMCTAgent (GPT-4 + GPT-4V)GPT-4 score74.24—Unverified
2Qwen2-VL-72BGPT-4 score74—Unverified
3InternVL2.5-78BGPT-4 score72.3—Unverified
4GPT-4o +text rationale +IoTGPT-4 score72.2—Unverified
5Lyra-ProGPT-4 score71.4—Unverified
6GLM-4V-PlusGPT-4 score71.1—Unverified
7Phantom-7BGPT-4 score70.8—Unverified
8InternVL2.5-38BGPT-4 score68.8—Unverified
9InternVL2-26B (SGP, token ratio 64%)GPT-4 score65.6—Unverified
10Baichuan-Omni (7B)GPT-4 score65.4—Unverified