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 751–800 of 2167 papers

TitleStatusHype
Alignment, Mining and Fusion: Representation Alignment with Hard Negative Mining and Selective Knowledge Fusion for Medical Visual Question Answering—0
KNVQA: A Benchmark for evaluation knowledge-based VQA—0
KRISP: Integrating Implicit and Symbolic Knowledge for Open-Domain Knowledge-Based VQA—0
Knowledge-Based Visual Question Answering in Videos—0
HD-EPIC: A Highly-Detailed Egocentric Video Dataset—0
Knowledge Condensation and Reasoning for Knowledge-based VQA—0
Attention Mechanism based Cognition-level Scene Understanding—0
Hardware-Friendly Static Quantization Method for Video Diffusion Transformers—0
HAMMR: HierArchical MultiModal React agents for generic VQA—0
Prompting Medical Large Vision-Language Models to Diagnose Pathologies by Visual Question Answering—0
Knowledge Detection by Relevant Question and Image Attributes in Visual Question Answering—0
KVL-BERT: Knowledge Enhanced Visual-and-Linguistic BERT for Visual Commonsense Reasoning—0
Language bias in Visual Question Answering: A Survey and Taxonomy—0
LAPDoc: Layout-Aware Prompting for Documents—0
Learning by Hallucinating: Vision-Language Pre-training with Weak Supervision—0
Attention Guided Semantic Relationship Parsing for Visual Question Answering—0
`Just because you are right, doesn't mean I am wrong': Overcoming a bottleneck in development and evaluation of Open-Ended VQA tasks—0
KAT: A Knowledge Augmented Transformer for Vision-and-Language—0
Jointly Learning Truth-Conditional Denotations and Groundings using Parallel Attention—0
HAUR: Human Annotation Understanding and Recognition Through Text-Heavy Images—0
JTD-UAV: MLLM-Enhanced Joint Tracking and Description Framework for Anti-UAV Systems—0
Kernel Pooling for Convolutional Neural Networks—0
Guiding Visual Question Generation—0
HDR-ChipQA: No-Reference Quality Assessment on High Dynamic Range Videos—0
AlignVE: Visual Entailment Recognition Based on Alignment Relations—0
Guiding Visual Question Answering with Attention Priors—0
Connecting phases of matter to the flatness of the loss landscape in analog variational quantum algorithms—0
Connecting Language and Vision to Actions—0
Guiding Medical Vision-Language Models with Explicit Visual Prompts: Framework Design and Comprehensive Exploration of Prompt Variations—0
Hierarchical Memory for Long Video QA—0
Hierarchical Modeling for Medical Visual Question Answering with Cross-Attention Fusion—0
A Transformer-based Cross-modal Fusion Model with Adversarial Training for VQA Challenge 2021—0
Joint Image Captioning and Question Answering—0
Grounding Complex Navigational Instructions Using Scene Graphs—0
Grounding Chest X-Ray Visual Question Answering with Generated Radiology Reports—0
Highly Efficient No-reference 4K Video Quality Assessment with Full-Pixel Covering Sampling and Training Strategy—0
Grounding Answers for Visual Questions Asked by Visually Impaired People—0
HiTeA: Hierarchical Temporal-Aware Video-Language Pre-training—0
A Token-level Text Image Foundation Model for Document Understanding—0
How good are deep models in understanding the generated images?—0
Joint learning of object graph and relation graph for visual question answering—0
Generating and Evaluating Explanations of Attended and Error-Inducing Input Regions for VQA Models—0
Compressing Visual-linguistic Model via Knowledge Distillation—0
Grounded Word Sense Translation—0
It Takes Two to Tango: Towards Theory of AI's Mind—0
Griffon-G: Bridging Vision-Language and Vision-Centric Tasks via Large Multimodal Models—0
How to Design Sample and Computationally Efficient VQA Models—0
Co-VQA : Answering by Interactive Sub Question Sequence—0
A Thousand Words Are Worth More Than a Picture: Natural Language-Centric Outside-Knowledge Visual Question Answering—0
iVQA: Inverse Visual Question Answering—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