Abstract Visual Question Answering (VQA) is a significant cross-disciplinary issue in the
fields of computer vision and natural language processing that requires a computer to output …

The current review helpfulness prediction (RHP) methods simply rely on the textual features
and meta features to predict review helpfulness, overlooking the informational value of …

Understanding multimodal information is the key to visual question answering (VQA) tasks.
Most existing approaches use attention mechanisms to acquire fine-grained information …

The emergence of the Transformer optimizes the interactive modeling of multimodal
information in visual question answering (VQA) tasks, helping machines better understand …

Abstract Visual Question Answering (VQA) is a multimodal task that requires models to
understand both textual and visual information. Various VQA models have applied the …

The Earth's surface is continually changing, and identifying changes plays an important role
in urban planning and sustainability. Although change detection techniques have been …

Abstract Visual Question Answering (VQA) has made great progress recently due to the
increasing ability to understand and encode multi-modal inputs based on deep learning …

Recently, multimodal image-based methods have shown great performance in skin cancer
diagnosis. These methods usually use convolutional neural networks (CNNs) to extract the …

Multimodal learning from document data has achieved great success lately as it allows to
pre-train semantically meaningful features as a prior into a learnable downstream task. In …

Object detection has been a challenging task due to the complexity and diversity of objects.
The emergence of self-attention mechanism provides a new clue for feature fusion in object …