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 1051–1100 of 2167 papers

TitleStatusHype
Knowledge Condensation and Reasoning for Knowledge-based VQA—0
Mitigating Dialogue Hallucination for Large Vision Language Models via Adversarial Instruction Tuning—0
Few-Shot Image Classification and Segmentation as Visual Question Answering Using Vision-Language Models—0
Adversarial Training with OCR Modality Perturbation for Scene-Text Visual Question AnsweringCode0
UniCode: Learning a Unified Codebook for Multimodal Large Language Models—0
OmniCount: Multi-label Object Counting with Semantic-Geometric Priors—0
SnapNTell: Enhancing Entity-Centric Visual Question Answering with Retrieval Augmented Multimodal LLM—0
CLEVR-POC: Reasoning-Intensive Visual Question Answering in Partially Observable Environments—0
Enhancing Generalization in Medical Visual Question Answering Tasks via Gradient-Guided Model Perturbation—0
ArcSin: Adaptive ranged cosine Similarity injected noise for Language-Driven Visual Tasks—0
LLM-Assisted Multi-Teacher Continual Learning for Visual Question Answering in Robotic SurgeryCode0
CommVQA: Situating Visual Question Answering in Communicative ContextsCode0
A Spectrum Evaluation Benchmark for Medical Multi-Modal Large Language Models—0
VQAttack: Transferable Adversarial Attacks on Visual Question Answering via Pre-trained Models—0
II-MMR: Identifying and Improving Multi-modal Multi-hop Reasoning in Visual Question AnsweringCode0
PaLM2-VAdapter: Progressively Aligned Language Model Makes a Strong Vision-language Adapter—0
Prompt-based Personalized Federated Learning for Medical Visual Question Answering—0
LAPDoc: Layout-Aware Prompting for Documents—0
Pretraining Vision-Language Model for Difference Visual Question Answering in Longitudinal Chest X-raysCode0
Visual Question Answering Instruction: Unlocking Multimodal Large Language Model To Domain-Specific Visual Multitasks—0
CIC: A Framework for Culturally-Aware Image Captioning—0
Convincing Rationales for Visual Question Answering ReasoningCode0
Curriculum reinforcement learning for quantum architecture search under hardware errors—0
Knowledge Generation for Zero-shot Knowledge-based VQACode0
Proximity QA: Unleashing the Power of Multi-Modal Large Language Models for Spatial Proximity AnalysisCode0
Binding Touch to Everything: Learning Unified Multimodal Tactile Representations—0
Muffin or Chihuahua? Challenging Multimodal Large Language Models with Multipanel VQA—0
LCV2: An Efficient Pretraining-Free Framework for Grounded Visual Question Answering—0
Improving Data Augmentation for Robust Visual Question Answering with Effective Curriculum Learning—0
Free Form Medical Visual Question Answering in Radiology—0
SpatialVLM: Endowing Vision-Language Models with Spatial Reasoning Capabilities—0
Advancing Large Multi-modal Models with Explicit Chain-of-Reasoning and Visual Question Generation—0
COCO is "ALL'' You Need for Visual Instruction Fine-tuning—0
Video Quality Assessment Based on Swin TransformerV2 and Coarse to Fine Strategy—0
Uncovering the Full Potential of Visual Grounding Methods in VQACode0
BOK-VQA: Bilingual outside Knowledge-Based Visual Question Answering via Graph Representation Pretraining—0
Generalizing Visual Question Answering from Synthetic to Human-Written Questions via a Chain of QA with a Large Language ModelCode0
Hallucination Benchmark in Medical Visual Question AnsweringCode0
GRAM: Global Reasoning for Multi-Page VQA—0
Subjective and Objective Analysis of Indian Social Media Video QualityCode0
ArtQuest: Countering Hidden Language Biases in ArtVQACode0
Mask4Align: Aligned Entity Prompting with Color Masks for Multi-Entity Localization Problems—0
DIEM: Decomposition-Integration Enhancing Multimodal Insights—0
Unified-IO 2: Scaling Autoregressive Multimodal Models with Vision Language Audio and Action—0
Synthesize Step-by-Step: Tools Templates and LLMs as Data Generators for Reasoning-Based Chart VQA—0
Text-Conditioned Generative Model of 3D Strand-based Human Hairstyles—0
Multi-Prompts Learning with Cross-Modal Alignment for Attribute-based Person Re-Identification—0
Gemini Pro Defeated by GPT-4V: Evidence from Education—0
Knowledge Guided Semi-Supervised Learning for Quality Assessment of User Generated VideosCode0
Q-Boost: On Visual Quality Assessment Ability of Low-level Multi-Modality Foundation 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