With the advent of generative adversarial networks, synthesizing images from text
descriptions has recently become an active research area. It is a flexible and intuitive way for …

In recent years, deep neural networks have been successful in both industry and academia,
especially for computer vision tasks. The great success of deep learning is mainly due to its …

Synthesizing high-quality realistic images from text descriptions is a challenging task.
Existing text-to-image Generative Adversarial Networks generally employ a stacked …

Visual attention not only improves the performance of image captioners, but also serves as a
visual interpretation to qualitatively measure the caption rationality and model transparency …

Life-long learning aims at learning a sequence of tasks without forgetting the previously
acquired knowledge. However, the involved training data may not be life-long legitimate due …

Visible-infrared person re-identification (VI-ReID) task aims to retrieve the same person
between visible and infrared images. VI-ReID is challenging as the images captured by …

In this paper, we study a novel task that enables partial knowledge transfer from pre-trained
models, which we term as Partial Network Cloning (PNC). Unlike prior methods that update …

Event cameras sense per-pixel intensity changes and produce asynchronous event streams
with high dynamic range and less motion blur, showing advantages over the conventional …

Text-to-image synthesis is a fascinating area of research that aims to generate images
based on textual descriptions. The main goal of this field is to generate images that match …

Due to the development of Generative Adversarial Networks (GANs), significant progress
has been achieved in text-to-image synthesis task. However, most previous works have only …