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 951–1000 of 2167 papers

TitleStatusHype
Pyramid Coder: Hierarchical Code Generator for Compositional Visual Question Answering—0
Take A Step Back: Rethinking the Two Stages in Visual Reasoning—0
Improved Few-Shot Image Classification Through Multiple-Choice Questions—0
Exploring the Effectiveness of Object-Centric Representations in Visual Question Answering: Comparative Insights with Foundation Models—0
QuIIL at T3 challenge: Towards Automation in Life-Saving Intervention Procedures from First-Person ViewCode0
ProcTag: Process Tagging for Assessing the Efficacy of Document Instruction Data—0
Multimodal Reranking for Knowledge-Intensive Visual Question Answering—0
EchoSight: Advancing Visual-Language Models with Wiki Knowledge—0
TM-PATHVQA:90000+ Textless Multilingual Questions for Medical Visual Question Answering—0
Extracting Training Data from Document-Based VQA Models—0
Segmentation-guided Attention for Visual Question Answering from Remote Sensing Images—0
VQA-Diff: Exploiting VQA and Diffusion for Zero-Shot Image-to-3D Vehicle Asset Generation in Autonomous Driving—0
Large Language Models Understand LayoutCode0
CLIPVQA:Video Quality Assessment via CLIPCode0
Black-box Model Ensembling for Textual and Visual Question Answering via Information FusionCode0
Visual Robustness Benchmark for Visual Question Answering (VQA)Code0
BACON: Improving Clarity of Image Captions via Bag-of-Concept Graphs—0
MindBench: A Comprehensive Benchmark for Mind Map Structure Recognition and Analysis—0
D-Rax: Domain-specific Radiologic assistant leveraging multi-modal data and eXpert model predictions—0
https://arxiv.org/abs/2407.00634Code0
Certainly Uncertain: A Benchmark and Metric for Multimodal Epistemic and Aleatoric Awareness—0
μ-Bench: A Vision-Language Benchmark for Microscopy UnderstandingCode0
Hierarchical Memory for Long Video QA—0
SK-VQA: Synthetic Knowledge Generation at Scale for Training Context-Augmented Multimodal LLMs—0
Disentangling Knowledge-based and Visual Reasoning by Question Decomposition in KB-VQA—0
RAVEN: Multitask Retrieval Augmented Vision-Language Learning—0
Enhancing Continual Learning in Visual Question Answering with Modality-Aware Feature DistillationCode0
On the Role of Visual Grounding in VQA—0
MM-SpuBench: Towards Better Understanding of Spurious Biases in Multimodal LLMs—0
Priorformer: A UGC-VQA Method with content and distortion priors—0
Losing Visual Needles in Image Haystacks: Vision Language Models are Easily Distracted in Short and Long Contexts—0
Tri-VQA: Triangular Reasoning Medical Visual Question Answering for Multi-Attribute Analysis—0
VGA: Vision GUI Assistant -- Minimizing Hallucinations through Image-Centric Fine-TuningCode0
Biomedical Visual Instruction Tuning with Clinician Preference AlignmentCode0
Diversify, Rationalize, and Combine: Ensembling Multiple QA Strategies for Zero-shot Knowledge-based VQACode0
Beyond Raw Videos: Understanding Edited Videos with Large Multimodal ModelCode0
What is the Visual Cognition Gap between Humans and Multimodal LLMs?Code0
Precision Empowers, Excess Distracts: Visual Question Answering With Dynamically Infused Knowledge In Language Models—0
Optimizing Visual Question Answering Models for Driving: Bridging the Gap Between Human and Machine Attention Patterns—0
CVQA: Culturally-diverse Multilingual Visual Question Answering Benchmark—0
Composition Vision-Language Understanding via Segment and Depth Anything ModelCode0
Understanding Information Storage and Transfer in Multi-modal Large Language Models—0
Diffusion-Refined VQA Annotations for Semi-Supervised Gaze FollowingCode0
Translation Deserves Better: Analyzing Translation Artifacts in Cross-lingual Visual Question Answering—0
Mixture of Rationale: Multi-Modal Reasoning Mixture for Visual Question Answering—0
Selectively Answering Visual Questions—0
VQA Training Sets are Self-play Environments for Generating Few-shot Pools—0
Evaluating Zero-Shot GPT-4V Performance on 3D Visual Question Answering Benchmarks—0
PTM-VQA: Efficient Video Quality Assessment Leveraging Diverse PreTrained Models from the Wild—0
Privacy-Aware Visual Language Models—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