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 …

Cross-modal retrieval (CMR) enables flexible retrieval experience across different
modalities (eg, texts versus images), which maximally benefits us from the abundance of …

Recently, generative adversarial network (GAN) has shown its strong ability on modeling
data distribution via adversarial learning. Cross-modal GAN, which attempts to utilize the …

Generative adversarial networks (GANs) have demonstrated remarkable potential in the
realm of text-to-image synthesis. Nevertheless, conventional GANs employing conditional …

Audio-visual matching aims to learn the intrinsic correspondence between image and audio
clip. Existing works mainly concentrate on learning discriminative features, while ignore the …

Zero-shot learning (ZSL) aims to learn models that can recognize images of semantically
related unseen categories, through transferring attribute-based knowledge learned from …

Image and sentence matching has attracted increasing attention since it is associated with
two important modalities of vision and language. Previous methods aim to find the latent …

Text-to-image synthesis is to generate images with the consistent content as the given text
description, which is a highly challenging task with two main issues: visual reality and …

Cross-modal image-text retrieval is an important area of Vision-and-Language task that
models the similarity of image-text pairs by embedding features into a shared space for …

Generalized zero-shot learning (GZSL) aims to recognize both seen and unseen samples by
leveraging the connections between semantic and visual representations. Recently, a …