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

Showing 751800 of 2167 papers

TitleStatusHype
GPT-4V-AD: Exploring Grounding Potential of VQA-oriented GPT-4V for Zero-shot Anomaly DetectionCode1
From Image to Language: A Critical Analysis of Visual Question Answering (VQA) Approaches, Challenges, and Opportunities0
VQA-GEN: A Visual Question Answering Benchmark for Domain Generalization0
A Systematic Evaluation of GPT-4V's Multimodal Capability for Medical Image Analysis0
Language Guided Visual Question Answering: Elevate Your Multimodal Language Model Using Knowledge-Enriched PromptsCode1
Multimodal ChatGPT for Medical Applications: an Experimental Study of GPT-4VCode1
Dynamic Task and Weight Prioritization Curriculum Learning for Multimodal ImageryCode0
EHRXQA: A Multi-Modal Question Answering Dataset for Electronic Health Records with Chest X-ray ImagesCode1
3D-Aware Visual Question Answering about Parts, Poses and OcclusionsCode1
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 Generation0
Incorporating Probing Signals into Multimodal Machine Translation via Visual Question-Answering PairsCode0
Exploring Question Decomposition for Zero-Shot VQA0
Geometry-Aware Video Quality Assessment for Dynamic Digital Human0
Towards Perceiving Small Visual Details in Zero-shot Visual Question Answering with Multimodal LLMsCode1
Large Language Models are Temporal and Causal Reasoners for Video Question AnsweringCode1
LXMERT Model Compression for Visual Question AnsweringCode0
HallusionBench: An Advanced Diagnostic Suite for Entangled Language Hallucination and Visual Illusion in Large Vision-Language ModelsCode2
A Simple Baseline for Knowledge-Based Visual Question AnsweringCode0
RSAdapter: Adapting Multimodal Models for Remote Sensing Visual Question AnsweringCode0
UNK-VQA: A Dataset and a Probe into the Abstention Ability of Multi-modal Large ModelsCode0
VLIS: Unimodal Language Models Guide Multimodal Language GenerationCode1
PaLI-3 Vision Language Models: Smaller, Faster, StrongerCode1
Exploring Sparse Spatial Relation in Graph Inference for Text-Based VQA0
Open-Set Knowledge-Based Visual Question Answering with Inference PathsCode0
Improving mitosis detection on histopathology images using large vision-language models0
Jaeger: A Concatenation-Based Multi-Transformer VQA Model0
Off-Policy Evaluation for Human Feedback0
How (not) to ensemble LVLMs for VQA0
What If the TV Was Off? Examining Counterfactual Reasoning Abilities of Multi-modal Language ModelsCode1
Rephrase, Augment, Reason: Visual Grounding of Questions for Vision-Language ModelsCode1
Causal Reasoning through Two Layers of Cognition for Improving Generalization in Visual Question Answering0
Negative Object Presence Evaluation (NOPE) to Measure Object Hallucination in Vision-Language Models0
Improved Baselines with Visual Instruction TuningCode6
Improving Automatic VQA Evaluation Using Large Language Models0
On the Cognition of Visual Question Answering Models and Human Intelligence: A Comparative Study0
HallE-Control: Controlling Object Hallucination in Large Multimodal ModelsCode1
SelfGraphVQA: A Self-Supervised Graph Neural Network for Scene-based Question Answering0
Navigating Cultural Chasms: Exploring and Unlocking the Cultural POV of Text-To-Image ModelsCode0
Beyond Task Performance: Evaluating and Reducing the Flaws of Large Multimodal Models with In-Context LearningCode1
Fine-grained Late-interaction Multi-modal Retrieval for Retrieval Augmented Visual Question AnsweringCode2
ELIP: Efficient Language-Image Pre-training with Fewer Vision TokensCode0
Tackling VQA with Pretrained Foundation Models without Further Training0
InternLM-XComposer: A Vision-Language Large Model for Advanced Text-image Comprehension and CompositionCode0
Vulnerabilities in Video Quality Assessment Models: The Challenge of Adversarial AttacksCode1
Implicit Differentiable Outlier Detection Enable Robust Deep Multimodal AnalysisCode0
Sentence Attention Blocks for Answer Grounding0
Visual Question Answering in the Medical Domain0
Syntax Tree Constrained Graph Network for Visual Question Answering0
D3: Data Diversity Design for Systematic Generalization in Visual Question AnsweringCode0
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Benchmark Results

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