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 551–600 of 2167 papers

TitleStatusHype
YouMakeup VQA Challenge: Towards Fine-grained Action Understanding in Domain-Specific VideosCode1
Evaluating Multimodal Representations on Visual Semantic Textual SimilarityCode1
Pixel-BERT: Aligning Image Pixels with Text by Deep Multi-Modal TransformersCode1
Multi-Modal Graph Neural Network for Joint Reasoning on Vision and Scene TextCode1
X-Linear Attention Networks for Image CaptioningCode1
Ground Truth Evaluation of Neural Network Explanations with CLEVR-XAICode1
Counterfactual Samples Synthesizing for Robust Visual Question AnsweringCode1
PathVQA: 30000+ Questions for Medical Visual Question AnsweringCode1
Visual Commonsense R-CNNCode1
Hierarchical Conditional Relation Networks for Video Question AnsweringCode1
Multimodal fusion of imaging and genomics for lung cancer recurrence predictionCode1
Break It Down: A Question Understanding BenchmarkCode1
Fine-grained Image Classification and Retrieval by Combining Visual and Locally Pooled Textual FeaturesCode1
In Defense of Grid Features for Visual Question AnsweringCode1
Think Locally, Act Globally: Federated Learning with Local and Global RepresentationsCode1
Overcoming Data Limitation in Medical Visual Question AnsweringCode1
UNITER: UNiversal Image-TExt Representation LearningCode1
Don't Take the Easy Way Out: Ensemble Based Methods for Avoiding Known Dataset BiasesCode1
VL-BERT: Pre-training of Generic Visual-Linguistic RepresentationsCode1
LXMERT: Learning Cross-Modality Encoder Representations from TransformersCode1
VideoNavQA: Bridging the Gap between Visual and Embodied Question AnsweringCode1
VisualBERT: A Simple and Performant Baseline for Vision and LanguageCode1
ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language TasksCode1
OK-VQA: A Visual Question Answering Benchmark Requiring External KnowledgeCode1
Scene Text Visual Question AnsweringCode1
GQA: A New Dataset for Real-World Visual Reasoning and Compositional Question AnsweringCode1
Faithful Multimodal Explanation for Visual Question AnsweringCode1
R-VQA: Learning Visual Relation Facts with Semantic Attention for Visual Question AnsweringCode1
Compositional Attention Networks for Machine ReasoningCode1
AI2-THOR: An Interactive 3D Environment for Visual AICode1
Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environmentsCode1
FiLM: Visual Reasoning with a General Conditioning LayerCode1
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question AnsweringCode1
ParlAI: A Dialog Research Software PlatformCode1
Learning Cooperative Visual Dialog Agents with Deep Reinforcement LearningCode1
CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual ReasoningCode1
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based LocalizationCode1
Hierarchical Question-Image Co-Attention for Visual Question AnsweringCode1
Stacked Attention Networks for Image Question AnsweringCode1
VQA: Visual Question AnsweringCode1
VisionThink: Smart and Efficient Vision Language Model via Reinforcement LearningCode0
MGFFD-VLM: Multi-Granularity Prompt Learning for Face Forgery Detection with VLM—0
Evaluating Attribute Confusion in Fashion Text-to-Image Generation—0
LinguaMark: Do Multimodal Models Speak Fairly? A Benchmark-Based Evaluation—0
DrishtiKon: Multi-Granular Visual Grounding for Text-Rich Document ImagesCode0
SMMILE: An Expert-Driven Benchmark for Multimodal Medical In-Context Learning—0
Bridging Video Quality Scoring and Justification via Large Multimodal Models—0
HRIBench: Benchmarking Vision-Language Models for Real-Time Human Perception in Human-Robot InteractionCode0
FOCUS: Internal MLLM Representations for Efficient Fine-Grained Visual Question Answering—0
GEMeX-ThinkVG: Towards Thinking with Visual Grounding in Medical VQA via Reinforcement Learning—0
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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
9InternVL-CAccuracy81.2—Unverified
10LyricsAccuracy81.2—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