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Visual Question Answering

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Papers

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Showing 1501–1550 of 2177 papers

TitleStatusHype
Take A Step Back: Rethinking the Two Stages in Visual Reasoning—0
Taking a HINT: Leveraging Explanations to Make Vision and Language Models More Grounded—0
Talking to the brain: Using Large Language Models as Proxies to Model Brain Semantic Representation—0
Task-driven Visual Saliency and Attention-based Visual Question Answering—0
Task Formulation Matters When Learning Continuously: A Case Study in Visual Question Answering—0
Task-Oriented Feature Compression for Multimodal Understanding via Device-Edge Co-Inference—0
Task-Oriented Multi-User Semantic Communications—0
Task-Oriented Semantic Communication in Large Multimodal Models-based Vehicle Networks—0
Task Progressive Curriculum Learning for Robust Visual Question Answering—0
TA-Student VQA: Multi-Agents Training by Self-Questioning—0
Tell-and-Answer: Towards Explainable Visual Question Answering using Attributes and Captions—0
Tell Me the Evidence? Dual Visual-Linguistic Interaction for Answer Grounding—0
Test-Time Adaptation for Visual Document Understanding—0
Text-Aware Dual Routing Network for Visual Question Answering—0
Text-Conditioned Generative Model of 3D Strand-based Human Hairstyles—0
Text-Guided Coarse-to-Fine Fusion Network for Robust Remote Sensing Visual Question Answering—0
TextMatch: Enhancing Image-Text Consistency Through Multimodal Optimization—0
DuReader_vis: A Chinese Dataset for Open-domain Document Visual Question Answering—0
TextSquare: Scaling up Text-Centric Visual Instruction Tuning—0
Textually Enriched Neural Module Networks for Visual Question Answering—0
TextVidBench: A Benchmark for Long Video Scene Text Understanding—0
The Color of the Cat is Gray: 1 Million Full-Sentences Visual Question Answering (FSVQA)—0
The curse of language biases in remote sensing VQA: the role of spatial attributes, language diversity, and the need for clear evaluation—0
The Forgettable-Watcher Model for Video Question Answering—0
The Impact of Explanations on AI Competency Prediction in VQA—0
The meaning of "most" for visual question answering models—0
The Meaning of ``Most'' for Visual Question Answering Models—0
The Quest for Visual Understanding: A Journey Through the Evolution of Visual Question Answering—0
The VQA-Machine: Learning How to Use Existing Vision Algorithms to Answer New Questions—0
The Wisdom of MaSSeS: Majority, Subjectivity, and Semantic Similarity in the Evaluation of VQA—0
TI-JEPA: An Innovative Energy-based Joint Embedding Strategy for Text-Image Multimodal Systems—0
TinyDrive: Multiscale Visual Question Answering with Selective Token Routing for Autonomous Driving—0
TinyVQA: Compact Multimodal Deep Neural Network for Visual Question Answering on Resource-Constrained Devices—0
TM-PATHVQA:90000+ Textless Multilingual Questions for Medical Visual Question Answering—0
TokenFocus-VQA: Enhancing Text-to-Image Alignment with Position-Aware Focus and Multi-Perspective Aggregations on LVLMs—0
Toward 3D Spatial Reasoning for Human-like Text-based Visual Question Answering—0
Toward Effective Reinforcement Learning Fine-Tuning for Medical VQA in Vision-Language Models—0
Towards a Unified Model for Generating Answers and Explanations in Visual Question Answering—0
Towards Automated Error Analysis: Learning to Characterize Errors—0
Towards Causal VQA: Revealing and Reducing Spurious Correlations by Invariant and Covariant Semantic Editing—0
Towards Complex Document Understanding By Discrete Reasoning—0
Towards Developing a Multilingual and Code-Mixed Visual Question Answering System by Knowledge Distillation—0
Towards Escaping from Language Bias and OCR Error: Semantics-Centered Text Visual Question Answering—0
Towards General Purpose Vision Systems: An End-to-End Task-Agnostic Vision-Language Architecture—0
Towards Grounded Visual Spatial Reasoning in Multi-Modal Vision Language Models—0
Towards Human-Level Understanding of Complex Process Engineering Schematics: A Pedagogical, Introspective Multi-Agent Framework for Open-Domain Question Answering—0
Towards Models that Can See and Read—0
Towards Omnidirectional Reasoning with 360-R1: A Dataset, Benchmark, and GRPO-based Method—0
Towards Reasoning-Aware Explainable VQA—0
Towards Semantic Equivalence of Tokenization in Multimodal LLM—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