The research progress in multimodal learning has grown rapidly over the last decade in
several areas, especially in computer vision. The growing potential of multimodal data …

Image-text matching has been a hot research topic bridging the vision and language areas.
It remains challenging because the current representation of image usually lacks global …

Existing deep learning based de-raining approaches have resorted to the convolutional
architectures. However, the intrinsic limitations of convolution, including local receptive fields …

Although existing artistic style transfer methods have achieved significant improvement with
deep neural networks, they still suffer from artifacts such as disharmonious colors and …

With the springing up of face synthesis techniques, it is prominent in need to develop
powerful face forgery detection methods due to security concerns. Some existing methods …

In this work, we propose a new feed-forward arbitrary style transfer method, referred to as
StyleFormer, which can simultaneously fulfill fine-grained style diversity and semantic …

As a bridge between language and vision domains, cross-modal retrieval between images
and texts is a hot research topic in recent years. It remains challenging because the current …

We introduce a simple and versatile framework for image-to-image translation. We unearth
the importance of normalization layers, and provide a carefully designed two-stream …

One key challenge of exemplar-guided image generation lies in establishing fine-grained
correspondences between input and guided images. Prior approaches, despite the …

Zero-shot learning (ZSL) aims to recognize instances of unseen classes solely based on the
semantic descriptions of the classes. Existing algorithms usually formulate it as a semantic …