Visual Question Answering (VQA)
758 papers with code • 62 benchmarks • 112 datasets
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
Benchmarks
These leaderboards are used to track progress in Visual Question Answering (VQA)
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Libraries
Use these libraries to find Visual Question Answering (VQA) models and implementationsDatasets
MS COCO
Visual Genome
CLEVR
GQA
Visual Question Answering v2.0
Conceptual Captions
OK-VQA
TextVQA
VCR
VisDial
Subtasks
Latest papers
A Gaze-grounded Visual Question Answering Dataset for Clarifying Ambiguous Japanese Questions
Such ambiguities in questions are often clarified by the contexts in conversational situations, such as joint attention with a user or user gaze information.
Intrinsic Subgraph Generation for Interpretable Graph based Visual Question Answering
In this work, we introduce an interpretable approach for graph-based VQA and demonstrate competitive performance on the GQA dataset.
IllusionVQA: A Challenging Optical Illusion Dataset for Vision Language Models
GPT4V, the best-performing VLM, achieves 62. 99% accuracy (4-shot) on the comprehension task and 49. 7% on the localization task (4-shot and Chain-of-Thought).
MedPromptX: Grounded Multimodal Prompting for Chest X-ray Diagnosis
biomedia-mbzuai/medpromptx
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Chest X-ray images are commonly used for predicting acute and chronic cardiopulmonary conditions, but efforts to integrate them with structured clinical data face challenges due to incomplete electronic health records (EHR).
Multi-Agent VQA: Exploring Multi-Agent Foundation Models in Zero-Shot Visual Question Answering
bowen-upenn/Multi-Agent-VQA
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This work explores the zero-shot capabilities of foundation models in Visual Question Answering (VQA) tasks.
vid-TLDR: Training Free Token merging for Light-weight Video Transformer
mlvlab/vid-tldr
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To tackle these issues, we propose training free token merging for lightweight video Transformer (vid-TLDR) that aims to enhance the efficiency of video Transformers by merging the background tokens without additional training.
VL-ICL Bench: The Devil in the Details of Benchmarking Multimodal In-Context Learning
ys-zong/vl-icl
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Built on top of LLMs, vision large language models (VLLMs) have advanced significantly in areas such as recognition, reasoning, and grounding.
Adversarial Training with OCR Modality Perturbation for Scene-Text Visual Question Answering
Scene-Text Visual Question Answering (ST-VQA) aims to understand scene text in images and answer questions related to the text content.
Multi-modal Auto-regressive Modeling via Visual Words
pengts/vw-lmm
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Large Language Models (LLMs), benefiting from the auto-regressive modelling approach performed on massive unannotated texts corpora, demonstrates powerful perceptual and reasoning capabilities.
TextMonkey: An OCR-Free Large Multimodal Model for Understanding Document
We present TextMonkey, a large multimodal model (LMM) tailored for text-centric tasks.
