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 1101–1150 of 2167 papers

TitleStatusHype
Towards a Unified Multimodal Reasoning FrameworkCode0
LLM4VG: Large Language Models Evaluation for Video Grounding—0
Reducing Hallucinations: Enhancing VQA for Flood Disaster Damage Assessment with Visual Contexts—0
Object Attribute Matters in Visual Question AnsweringCode0
BloomVQA: Assessing Hierarchical Multi-modal Comprehension—0
Multi-Clue Reasoning with Memory Augmentation for Knowledge-based Visual Question Answering—0
Interactive Visual Task Learning for Robots—0
VQA4CIR: Boosting Composed Image Retrieval with Visual Question AnsweringCode0
Full-reference Video Quality Assessment for User Generated Content Transcoding—0
An Evaluation of GPT-4V and Gemini in Online VQA—0
M^2ConceptBase: A Fine-Grained Aligned Concept-Centric Multimodal Knowledge BaseCode0
Advancing Surgical VQA with Scene Graph Knowledge—0
RankDVQA-mini: Knowledge Distillation-Driven Deep Video Quality Assessment—0
BESTMVQA: A Benchmark Evaluation System for Medical Visual Question Answering—0
Causal-CoG: A Causal-Effect Look at Context Generation for Boosting Multi-modal Language Models—0
Lyrics: Boosting Fine-grained Language-Vision Alignment and Comprehension via Semantic-aware Visual Objects—0
On the Robustness of Large Multimodal Models Against Image Adversarial Attacks—0
Visual Program Distillation: Distilling Tools and Programmatic Reasoning into Vision-Language Models—0
MedXChat: A Unified Multimodal Large Language Model Framework towards CXRs Understanding and Generation—0
Unleashing the Potential of Large Language Model: Zero-shot VQA for Flood Disaster Scenario—0
Omni-SMoLA: Boosting Generalist Multimodal Models with Soft Mixture of Low-rank Experts—0
Zero-Shot Video Question Answering with Procedural Programs—0
Towards Top-Down Reasoning: An Explainable Multi-Agent Approach for Visual Question Answering—0
The curse of language biases in remote sensing VQA: the role of spatial attributes, language diversity, and the need for clear evaluation—0
Fully Authentic Visual Question Answering Dataset from Online CommunitiesCode0
From Wrong To Right: A Recursive Approach Towards Vision-Language Explanation—0
KNVQA: A Benchmark for evaluation knowledge-based VQA—0
Filling the Image Information Gap for VQA: Prompting Large Language Models to Proactively Ask QuestionsCode0
Understanding and Mitigating Classification Errors Through Interpretable Token Patterns—0
Attribute Diversity Determines the Systematicity Gap in VQACode0
Improving Zero-shot Visual Question Answering via Large Language Models with Reasoning Question PromptsCode0
Multiple-Question Multiple-Answer Text-VQA—0
Asking More Informative Questions for Grounded Retrieval—0
CLiF-VQA: Enhancing Video Quality Assessment by Incorporating High-Level Semantic Information related to Human Feelings—0
What Large Language Models Bring to Text-rich VQA?—0
Visual Commonsense based Heterogeneous Graph Contrastive Learning—0
Analyzing Modular Approaches for Visual Question DecompositionCode0
Improving Vision-and-Language Reasoning via Spatial Relations Modeling—0
Zero-shot Translation of Attention Patterns in VQA Models to Natural LanguageCode0
From Image to Language: A Critical Analysis of Visual Question Answering (VQA) Approaches, Challenges, and Opportunities—0
VQA-GEN: A Visual Question Answering Benchmark for Domain Generalization—0
A Systematic Evaluation of GPT-4V's Multimodal Capability for Medical Image Analysis—0
Dynamic Task and Weight Prioritization Curriculum Learning for Multimodal ImageryCode0
ViCLEVR: A Visual Reasoning Dataset and Hybrid Multimodal Fusion Model for Visual Question Answering in VietnameseCode0
Davidsonian Scene Graph: Improving Reliability in Fine-grained Evaluation for Text-to-Image Generation—0
Incorporating Probing Signals into Multimodal Machine Translation via Visual Question-Answering PairsCode0
Exploring Question Decomposition for Zero-Shot VQA—0
Geometry-Aware Video Quality Assessment for Dynamic Digital Human—0
LXMERT Model Compression for Visual Question AnsweringCode0
A Simple Baseline for Knowledge-Based Visual Question AnsweringCode0
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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