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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
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
VisionThink: Smart and Efficient Vision Language Model via Reinforcement Learning
MGFFD-VLM: Multi-Granularity Prompt Learning for Face Forgery Detection with VLM
Describe Anything Model for Visual Question Answering on Text-rich Images
MindJourney: Test-Time Scaling with World Models for Spatial Reasoning
EmbRACE-3K: Embodied Reasoning and Action in Complex Environments
Warehouse Spatial Question Answering with LLM Agent
M2-Reasoning: Empowering MLLMs with Unified General and Spatial Reasoning
ByDeWay: Boost Your multimodal LLM with DEpth prompting in a Training-Free Way
Scaling RL to Long Videos
OST-Bench: Evaluating the Capabilities of MLLMs in Online Spatio-temporal Scene Understanding
A Neural Representation Framework with LLM-Driven Spatial Reasoning for Open-Vocabulary 3D Visual Grounding
Evaluating Attribute Confusion in Fashion Text-to-Image Generation
LinguaMark: Do Multimodal Models Speak Fairly? A Benchmark-Based Evaluation
MagiC: Evaluating Multimodal Cognition Toward Grounded Visual Reasoning
Barriers in Integrating Medical Visual Question Answering into Radiology Workflows: A Scoping Review and Clinicians' Insights
Enhancing Scientific Visual Question Answering through Multimodal Reasoning and Ensemble Modeling
ReLoop: "Seeing Twice and Thinking Backwards" via Closed-loop Training to Mitigate Hallucinations in Multimodal understanding
Decoupled Seg Tokens Make Stronger Reasoning Video Segmenter and Grounder
Revisiting CroPA: A Reproducibility Study and Enhancements for Cross-Prompt Adversarial Transferability in Vision-Language Models
Optimising Language Models for Downstream Tasks: A Post-Training Perspective
Bridging Video Quality Scoring and Justification via Large Multimodal Models
World-aware Planning Narratives Enhance Large Vision-Language Model Planner
DrishtiKon: Multi-Granular Visual Grounding for Text-Rich Document Images
SMMILE: An Expert-Driven Benchmark for Multimodal Medical In-Context Learning
ShotBench: Expert-Level Cinematic Understanding in Vision-Language Models
ImplicitQA: Going beyond frames towards Implicit Video Reasoning
From 2D to 3D Cognition: A Brief Survey of General World Models
Semantic-enhanced Modality-asymmetric Retrieval for Online E-commerce Search
HRIBench: Benchmarking Vision-Language Models for Real-Time Human Perception in Human-Robot Interaction
Video Perception Models for 3D Scene Synthesis
MMSearch-R1: Incentivizing LMMs to Search
FOCUS: Internal MLLM Representations for Efficient Fine-Grained Visual Question Answering
ToSA: Token Merging with Spatial Awareness
GEMeX-ThinkVG: Towards Thinking with Visual Grounding in Medical VQA via Reinforcement Learning
Scene-R1: Video-Grounded Large Language Models for 3D Scene Reasoning without 3D Annotations
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