Papers Visual Question Answering
“Visual Question Answering” 태그가 달린 논문 2,881편 · 필터 해제
UniWAM: Unified World-Action Model
Vision-language-action models benefit from the understanding and reasoning capabilities of pretrained vision-language models, but action-only supervision provides limited grounding in world dynamics. Conversely, world-ac…
Visual Question AnsweringInstruction FollowingVideo GenerationFusing Visual and Textual Representations via Multi-layer Fusing Transformers for Vietnamese Visual Question Answering
In recent decades, artificial intelligence has made significant progress in understanding and interacting with images. One of the important applications of this technology is Visual Question Answering (VQA), a research f…
Visual Question AnsweringTables Decoded: DELTA for Structure, TARQA for Understanding
Table understanding is a core task in document intelligence, encompassing two key subtasks: table reconstruction and table visual question answering (TabVQA). While recent approaches predominantly rely on vision- languag…
Visual Question AnsweringBrainFocus: EEG-Guided ROI Selection for Efficient Vision-Language Models
Vision-language models (VLMs) achieve strong visual question answering (VQA) performance, but processing large cluttered images is computationally expensive when only a small region is relevant. Electroencephalography (E…
Visual Question AnsweringDriveMCP: An Agentic AI framework for Advanced Driver Assistance System
An agentic AI driver-assistance framework that integrates perception, compliance reasoning, vehicle-state interpretation, and safety arbitration into a modular and auditable pipeline. The architecture, referred to as Dri…
Visual Question AnsweringScene UnderstandingNot Another Text Benchmark: Putting the "Visual" Back in Visual Question Answering for Large Video Models
Large video models have exhibited impressive performance on a wide range of visual question answering tasks, owing to the rise of powerful, pretrained text and vision encoders. The usefulness of such models have also bee…
Visual Question AnsweringSAVOR: Self-Aware Visual Grounding via Confidence-Calibrated Reinforcement Learning for Multimodal Hallucination Mitigation
Multimodal large language models (MLLMs) have made strong progress on visual question answering and image captioning, yet they still produce fluent claims about objects, attributes, or relations that are not grounded in …
Visual Question AnsweringReinforcement LearningVisual GroundingImage CaptioningCounterfactual Reasoning for Robust Visual Question Answering
Modern Visual Question Answering (VQA) models often exploit spurious correlations in training data, leading to poor out-of-distribution (OOD) generalization due to language bias. Although counterfactual learning has show…
Visual Question AnsweringContrastive LearningVisual GroundingVision And Text Transformer For Predicting Answerability On Visual Question Answering
Answerability on Visual Question Answering is a novel and attractive task to predict answerable scores between images and questions in multi-modal data. Existing works often utilize a binary mapping from visual question …
Visual Question AnsweringBeyond Surface Imitation: Contrastive Modeling for Reasoning Path Alignment in Multimodal In-Context Learning
In-context learning (ICL) is widely used in multimodal large language models (MLLMs) and achieves strong performance across a wide range of multimodal tasks. However, existing multimodal ICL methods often rely on surface…
Visual Question AnsweringVDiff-Bench: A Challenging Benchmark for Fine-Grained Image Difference Identification
Multimodal Large Language Models (MLLMs) perform strongly on general visual understanding tasks such as visual question answering, yet they often struggle with a basic comparative skill: identifying what has changed betw…
Visual Question AnsweringImageEval 2026: Culturally Grounded Arabic Multimodal Evaluation
We present an overview of the ImageEval 2026 shared task on culturally grounded Arabic multimodal evaluation. It includes two tasks: (i) AynVQA, covering spoken visual question answering and image-grounded hallucination …
Visual Question AnsweringText-to-Image GenerationQwen-Drive-1.0: An Initial Step towards a Vision-Language Foundation Model for Autonomous Driving
We present Qwen-Drive-1.0, an initial step towards a vision-language foundation model for autonomous driving. Qwen-Drive-1.0 retains the architecture of the pretrained vision-language model (VLM) and integrates 3D percep…
Visual Question AnsweringScene Understanding3D Object DetectionAutonomous DrivingAn Integrated Vision-and-Language Pretraining (VLP) and Visual Question Answering (VQA) model to Automate Nondestructive Evaluation Image Analysis
An AI-based approach called ChatNDE Figure to Caption is introduced, which aims to automate the interpretation of NDE images using deep learning and natural language processing (NLP). A Vision-and-Language Pretraining (V…
Visual Question AnsweringSynth-JDoc: Synthesizing a Japanese Document Image Dataset for OCR with Diverse Layouts and Embedded Images
The ability of Large Vision Language Models (LVLMs) to read text within document images is crucial, as it enables various applications such as Document Visual Question Answering. To enhance the text-reading capabilities …
Visual Question AnsweringFrom Reasoning to Pixels: Grounded Medical Multimodal LLMs for VQA and Segmentation
Although Multimodal Large Language Models (MLLMs) have demonstrated impressive performance in Medical Visual Question Answering (Med-VQA), their reliance on global image features often lacks precise pixel-level grounding…
Visual Question AnsweringMedFG-VQA: Low-Frequency Memory and Graph Attention for Lightweight Medical VQA
Medical Visual Question Answering (Med-VQA) holds significant promise for clinical decision support, yet faces challenges due to limited annotated data and the high computational demands of existing large vision-language…
Visual Question AnsweringAWM: Answerable Working Memory for Long-Document VQA Agents
Long-document visual question answering increasingly relies on VLM agents that retrieve candidate pages, inspect page images, write findings to working memory, and synthesize answers. Working memory should carry answer-s…
Visual Question AnsweringLlama-Mobile: Efficient 2.7-Bit Quantization of VLMs
Deploying vision-language models (VLMs) on mobile devices is challenging due to their significant memory and compute requirements. We present a framework for quantizing VLMs for efficient inference on resource-constraine…
Visual Question AnsweringMasking Is Not Enough: Generative Restoration for Multimodal De-Identification in Medical AI
Medical image-text data can expose protected health information (PHI) through both visible image content as well as accompanying text, creating a barrier to privacy-preserving medical AI systems. This risk is especially …
Visual Question Answering