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 901–950 of 2167 papers

TitleStatusHype
Good, Better, Best: Textual Distractors Generation for Multiple-Choice Visual Question Answering via Reinforcement Learning—0
ISAAQ - Mastering Textbook Questions with Pre-trained Transformers and Bottom-Up and Top-Down Attention—0
Astrea: A MOE-based Visual Understanding Model with Progressive Alignment—0
Aligned Vector Quantization for Edge-Cloud Collabrative Vision-Language Models—0
Deep Equilibrium Multimodal Fusion—0
Iterated learning for emergent systematicity in VQA—0
It Takes Two to Tango: Towards Theory of AI's Mind—0
iVQA: Inverse Visual Question Answering—0
Jaeger: A Concatenation-Based Multi-Transformer VQA Model—0
Goal-Oriented Semantic Communication for Wireless Visual Question Answering—0
Commonsense Video Question Answering through Video-Grounded Entailment Tree Reasoning—0
Getting ViT in Shape: Scaling Laws for Compute-Optimal Model Design—0
ComicsPAP: understanding comic strips by picking the correct panel—0
Jointly Learning Truth-Conditional Denotations and Groundings using Parallel Attention—0
GeoRSMLLM: A Multimodal Large Language Model for Vision-Language Tasks in Geoscience and Remote Sensing—0
Geometry-Aware Video Quality Assessment for Dynamic Digital Human—0
JTD-UAV: MLLM-Enhanced Joint Tracking and Description Framework for Anti-UAV Systems—0
Evaluating and Improving Interactions with Hazy Oracles—0
Assisting Scene Graph Generation with Self-Supervision—0
Localize, Group, and Select: Boosting Text-VQA by Scene Text Modeling—0
Generative Visual Question Answering—0
KAT: A Knowledge Augmented Transformer for Vision-and-Language—0
Kernel Pooling for Convolutional Neural Networks—0
DePlot: One-shot visual language reasoning by plot-to-table translation—0
Combining Knowledge Graph and LLMs for Enhanced Zero-shot Visual Question Answering—0
Knowing Where to Look? Analysis on Attention of Visual Question Answering System—0
Assessment of Subjective and Objective Quality of Live Streaming Sports Videos—0
Knowledge Acquisition for Visual Question Answering via Iterative Querying—0
Knowledge-Based Counterfactual Queries for Visual Question Answering—0
Generating Triples with Adversarial Networks for Scene Graph Construction—0
Generating Rationales in Visual Question Answering—0
Knowledge Condensation and Reasoning for Knowledge-based VQA—0
Assessing Visual Quality of Omnidirectional Videos—0
Knowledge Detection by Relevant Question and Image Attributes in Visual Question Answering—0
Ontology-based knowledge representation for bone disease diagnosis: a foundation for safe and sustainable medical artificial intelligence systems—0
Generating Natural Questions from Images for Multimodal Assistants—0
Generating Natural Language Explanations for Visual Question Answering using Scene Graphs and Visual Attention—0
KNVQA: A Benchmark for evaluation knowledge-based VQA—0
COIN: Counterfactual Image Generation for VQA Interpretation—0
Generate then Select: Open-ended Visual Question Answering Guided by World Knowledge—0
Assessing the Robustness of Visual Question Answering Models—0
Generalized Hadamard-Product Fusion Operators for Visual Question Answering—0
Generalization Differences between End-to-End and Neuro-Symbolic Vision-Language Reasoning Systems—0
Assessing Image Quality Issues for Real-World Problems—0
Aligned Dual Channel Graph Convolutional Network for Visual Question Answering—0
LLaVA-Ultra: Large Chinese Language and Vision Assistant for Ultrasound—0
LLM4VG: Large Language Models Evaluation for Video Grounding—0
Localizing Before Answering: A Hallucination Evaluation Benchmark for Grounded Medical Multimodal LLMs—0
Language bias in Visual Question Answering: A Survey and Taxonomy—0
Gender and Racial Bias in Visual Question Answering Datasets—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