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 field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

We study on joint learning of Convolutional Neural Network (CNN) and Transformer for
vision-language pre-training (VLPT) which aims to learn cross-modal alignments from …

Zero-shot learning (ZSL), an emerging topic in recent years, targets at distinguishing unseen
class images by taking images from seen classes for training the classifier. Existing works …

Attention mechanisms are widely used in current encoder/decoder frameworks of image
captioning, where a weighted average on encoded vectors is generated at each time step to …

Few-shot classification aims to recognize unlabeled samples from unseen classes given
only few labeled samples. The unseen classes and low-data problem make few-shot …

In humans, Attention is a core property of all perceptual and cognitive operations. Given our
limited ability to process competing sources, attention mechanisms select, modulate, and …

Visual place recognition is a challenging task for applications such as autonomous driving
navigation and mobile robot localization. Distracting elements presenting in complex scenes …

Text-image cross-modal retrieval is a challenging task in the field of language and vision.
Most previous approaches independently embed images and sentences into a joint …

In order to answer semantically-complicated questions about an image, a Visual Question
Answering (VQA) model needs to fully understand the visual scene in the image, especially …