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 11511200 of 2167 papers

TitleStatusHype
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
Exploring Sparse Spatial Relation in Graph Inference for Text-Based VQA0
Open-Set Knowledge-Based Visual Question Answering with Inference PathsCode0
Jaeger: A Concatenation-Based Multi-Transformer VQA Model0
Off-Policy Evaluation for Human Feedback0
Improving mitosis detection on histopathology images using large vision-language models0
How (not) to ensemble LVLMs for VQA0
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
Improving Automatic VQA Evaluation Using Large Language Models0
On the Cognition of Visual Question Answering Models and Human Intelligence: A Comparative Study0
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
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
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
Interpretable Visual Question Answering via Reasoning Supervision0
S3C: Semi-Supervised VQA Natural Language Explanation via Self-Critical Learning0
Distraction-free Embeddings for Robust VQA0
Separate and Locate: Rethink the Text in Text-based Visual Question AnsweringCode0
VQA Therapy: Exploring Answer Differences by Visually Grounding AnswersCode0
UGC Quality Assessment: Exploring the Impact of Saliency in Deep Feature-Based Quality AssessmentCode0
Ada-DQA: Adaptive Diverse Quality-aware Feature Acquisition for Video Quality Assessment0
Making the V in Text-VQA Matter0
Bridging the Gap: Exploring the Capabilities of Bridge-Architectures for Complex Visual Reasoning Tasks0
Capturing Co-existing Distortions in User-Generated Content for No-reference Video Quality Assessment0
Workshop on Document Intelligence Understanding0
Context-VQA: Towards Context-Aware and Purposeful Visual Question AnsweringCode0
BARTPhoBEiT: Pre-trained Sequence-to-Sequence and Image Transformers Models for Vietnamese Visual Question Answering0
LOIS: Looking Out of Instance Semantics for Visual Question Answering0
Robust Visual Question Answering: Datasets, Methods, and Future Challenges0
NTIRE 2023 Quality Assessment of Video Enhancement Challenge0
A reinforcement learning approach for VQA validation: an application to diabetic macular edema grading0
Towards a performance analysis on pre-trained Visual Question Answering models for autonomous drivingCode0
Generative Visual Question Answering0
Let's ViCE! Mimicking Human Cognitive Behavior in Image Generation Evaluation0
Divide, Evaluate, and Refine: Evaluating and Improving Text-to-Image Alignment with Iterative VQA Feedback0
Subjective and Objective Audio-Visual Quality Assessment for User Generated ContentCode0
UIT-Saviors at MEDVQA-GI 2023: Improving Multimodal Learning with Image Enhancement for Gastrointestinal Visual Question Answering0
DoReMi: Grounding Language Model by Detecting and Recovering from Plan-Execution Misalignment0
Lightweight Recurrent Cross-modal Encoder for Video Question AnsweringCode0
Deep Equilibrium Multimodal Fusion0
Pre-Training Multi-Modal Dense Retrievers for Outside-Knowledge Visual Question AnsweringCode0
Visual Question Answering in Remote Sensing with Cross-Attention and Multimodal Information Bottleneck0
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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