CLEVR-POC: Reasoning-Intensive Visual Question Answering in Partially Observable Environments
시각적 질문 응답에서 추론 중심의 CLEVR-POC 벤치마크의 중요성
Q&A Prompts: Discovering Rich Visual Clues for VQA
Collecting rich visual clues enhances reasoning in VQA tasks.
Advancing Visual Question Answering: Exploring Generative Adversarial Networks, Autoencoders, and Attention Mechanisms
This study explores innovative methods, including Generative Adversarial Networks (GANs), autoencoders, and attention mechanisms, to improve the performance of Visual Question Answering (VQA) systems.
Enhancing Visual Question Answering through Comparative Analysis and Convolutional Textual Feature Extraction
Employing convolutional layers to extract multi-scale local textual features can improve performance on Visual Question Answering tasks compared to complex sequential models.
Enhancing Visual Question Answering through Ranking-Based Hybrid Training and Multimodal Fusion
The RankVQA model leverages a ranking-inspired hybrid training strategy and sophisticated multimodal fusion techniques to significantly enhance the performance of Visual Question Answering systems.
Comprehensive Analysis of Visual Question Answering: Datasets, Methods, and Emerging Trends
Visual Question Answering (VQA) is a rapidly evolving field that combines elements of computer vision and natural language processing to generate answers to questions about visual inputs. This survey provides a comprehensive overview of the VQA domain, including its applications, problem definitions, datasets, methods, and emerging trends.
