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 1451–1475 of 2167 papers

TitleStatusHype
Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling—0
Expanding the Boundaries of Vision Prior Knowledge in Multi-modal Large Language Models—0
Explanation vs Attention: A Two-Player Game to Obtain Attention for VQA—0
Explicit Bias Discovery in Visual Question Answering Models—0
Explicit Knowledge-based Reasoning for Visual Question Answering—0
Explicit Reasoning over End-to-End Neural Architectures for Visual Question Answering—0
Explore before Moving: A Feasible Path Estimation and Memory Recalling Framework for Embodied Navigation—0
Exploring Advanced Techniques for Visual Question Answering: A Comprehensive Comparison—0
Exploring Diverse Methods in Visual Question Answering—0
Exploring Human-like Attention Supervision in Visual Question Answering—0
Exploring Question Decomposition for Zero-Shot VQA—0
Exploring Sparse Spatial Relation in Graph Inference for Text-Based VQA—0
Exploring the Effectiveness of Object-Centric Representations in Visual Question Answering: Comparative Insights with Foundation Models—0
Exploring Weaknesses of VQA Models through Attribution Driven Insights—0
Extending Class Activation Mapping Using Gaussian Receptive Field—0
EKTVQA: Generalized use of External Knowledge to empower Scene Text in Text-VQA—0
Extracting Training Data from Document-Based VQA Models—0
EyeFound: A Multimodal Generalist Foundation Model for Ophthalmic Imaging—0
Look Before You Decide: Prompting Active Deduction of MLLMs for Assumptive Reasoning—0
EyeSim-VQA: A Free-Energy-Guided Eye Simulation Framework for Video Quality Assessment—0
F^3OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics—0
F^3OCUS - Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics—0
FashionVQA: A Domain-Specific Visual Question Answering System—0
Fast or Slow? Integrating Fast Intuition and Deliberate Thinking for Enhancing Visual Question Answering—0
Feature4X: Bridging Any Monocular Video to 4D Agentic AI with Versatile Gaussian Feature Fields—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