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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 1101–1150 of 2167 papers

TitleStatusHype
VQA-GEN: A Visual Question Answering Benchmark for Domain Generalization—0
VQA-GNN: Reasoning with Multimodal Knowledge via Graph Neural Networks for Visual Question Answering—0
VQA-LOL: Visual Question Answering under the Lens of Logic—0
VQA-MHUG: A Gaze Dataset to Study Multimodal Neural Attention in Visual Question Answering—0
VQA Training Sets are Self-play Environments for Generating Few-shot Pools—0
VQAttack: Transferable Adversarial Attacks on Visual Question Answering via Pre-trained Models—0
VQA with Cascade of Self- and Co-Attention Blocks—0
VSA4VQA: Scaling a Vector Symbolic Architecture to Visual Question Answering on Natural Images—0
Watching the News: Towards VideoQA Models that can Read—0
Weakly Supervised Visual Question Answer Generation—0
Weak Supervision helps Emergence of Word-Object Alignment and improves Vision-Language Tasks—0
Webly Supervised Concept Expansion for General Purpose Vision Models—0
What is needed for simple spatial language capabilities in VQA?—0
What Large Language Models Bring to Text-rich VQA?—0
What makes a good metric? Evaluating automatic metrics for text-to-image consistency—0
When are Lemons Purple? The Concept Association Bias of Vision-Language Models—0
Where is this coming from? Making groundedness count in the evaluation of Document VQA models—0
Where To Look: Focus Regions for Visual Question Answering—0
Which Client is Reliable?: A Reliable and Personalized Prompt-based Federated Learning for Medical Image Question Answering—0
Why context matters in VQA and Reasoning: Semantic interventions for VLM input modalities—0
Why Does a Visual Question Have Different Answers?—0
Why Does the VQA Model Answer No?: Improving Reasoning through Visual and Linguistic Inference—0
WoLF: Wide-scope Large Language Model Framework for CXR Understanding—0
Workshop on Document Intelligence Understanding—0
WSI-LLaVA: A Multimodal Large Language Model for Whole Slide Image—0
WuDaoMM: A large-scale Multi-Modal Dataset for Pre-training models—0
XGPT: Cross-modal Generative Pre-Training for Image Captioning—0
xGQA: Cross-Lingual Visual Question Answering—0
Yin and Yang: Balancing and Answering Binary Visual Questions—0
YouMakeup: A Large-Scale Domain-Specific Multimodal Dataset for Fine-Grained Semantic Comprehension—0
ZALM3: Zero-Shot Enhancement of Vision-Language Alignment via In-Context Information in Multi-Turn Multimodal Medical Dialogue—0
Zero-Shot Anomaly Detection in Battery Thermal Images Using Visual Question Answering with Prior Knowledge—0
Zero-Shot Transfer VQA Dataset—0
Zero-Shot Video Question Answering with Procedural Programs—0
Zero-Shot Visual Question Answering—0
Zero-Shot Visual Reasoning by Vision-Language Models: Benchmarking and Analysis—0
Bidirectional Contrastive Split Learning for Visual Question Answering—0
Generating Question Relevant Captions to Aid Visual Question Answering—0
Explainable High-order Visual Question Reasoning: A New Benchmark and Knowledge-routed Network—0
Does CLIP Benefit Visual Question Answering in the Medical Domain as Much as it Does in the General Domain?—0
Prompting Medical Large Vision-Language Models to Diagnose Pathologies by Visual Question Answering—0
Ontology-based knowledge representation for bone disease diagnosis: a foundation for safe and sustainable medical artificial intelligence systems—0
2nd Place Solution to the GQA Challenge 2019—0
3D Concept Learning and Reasoning from Multi-View Images—0
3D-CT-GPT: Generating 3D Radiology Reports through Integration of Large Vision-Language Models—0
3D Question Answering—0
ABC: Achieving Better Control of Multimodal Embeddings using VLMs—0
ABC-CNN: An Attention Based Convolutional Neural Network for Visual Question Answering—0
Abduction of Domain Relationships from Data for VQA—0
A Causal Approach to Mitigate Modality Preference Bias in Medical 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