SOTAVerified

Visual Question Answering (VQA)

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

Papers

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Showing 2076–2100 of 2167 papers

TitleStatusHype
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
TinyDrive: Multiscale Visual Question Answering with Selective Token Routing for Autonomous Driving—0
TinyRS-R1: Compact Multimodal Language Model for Remote Sensing—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
Toward Robust Hyper-Detailed Image Captioning: A Multiagent Approach and Dual Evaluation Metrics for Factuality and Coverage—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 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 Reasoning-Aware Explainable VQA—0
Towards Top-Down Reasoning: An Explainable Multi-Agent Approach for Visual Question Answering—0
Towards Transparent AI Systems: Interpreting Visual Question Answering Models—0
Towards Unsupervised Visual Reasoning: Do Off-The-Shelf Features Know How to Reason?—0
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All datasetsGQA Test2019VQA v2 test-devVQA v2 test-stdOK-VQAMSVD-QADocVQA testMSRVTT-QAInfographicVQAGQA test-devVizWiz 2020 VQAA-OKVQACLEVR

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1humanAccuracy89.3—Unverified
2DREAM+Unicoder-VL (MSRA)Accuracy76.04—Unverified
3TRRNet (Ensemble)Accuracy74.03—Unverified
4MIL-nbgaoAccuracy73.81—Unverified
5Kakao BrainAccuracy73.33—Unverified
6Coarse-to-Fine Reasoning, Single ModelAccuracy72.14—Unverified
7270Accuracy70.23—Unverified
8NSM ensemble (updated)Accuracy67.55—Unverified
9VinVL-DPTAccuracy64.92—Unverified
10VinVL+LAccuracy64.85—Unverified
#ModelMetricClaimedVerifiedStatus
1PaLIAccuracy84.3—Unverified
2BEiT-3Accuracy84.19—Unverified
3VLMoAccuracy82.78—Unverified
4ONE-PEACEAccuracy82.6—Unverified
5mPLUG (Huge)Accuracy82.43—Unverified
6CuMo-7BAccuracy82.2—Unverified
7X2-VLM (large)Accuracy81.9—Unverified
8MMUAccuracy81.26—Unverified
9LyricsAccuracy81.2—Unverified
10InternVL-CAccuracy81.2—Unverified
#ModelMetricClaimedVerifiedStatus
1BEiT-3overall84.03—Unverified
2mPLUG-Hugeoverall83.62—Unverified
3ONE-PEACEoverall82.52—Unverified
4X2-VLM (large)overall81.8—Unverified
5VLMooverall81.3—Unverified
6SimVLMoverall80.34—Unverified
7X2-VLM (base)overall80.2—Unverified
8VASToverall80.19—Unverified
9VALORoverall78.62—Unverified
10Prompt Tuningoverall78.53—Unverified