Generative adversarial networks (GANs) have been extensively studied in the past few
years. Arguably their most significant impact has been in the area of computer vision where …

Deep learning applies multiple processing layers to learn representations of data with
multiple levels of feature extraction. This emerging technique has reshaped the research …

Despite the large volume of face recognition datasets, there is a significant portion of
subjects, of which the samples are insufficient and thus under-represented. Ignoring such …

Face recognition (FR) is one of the most popular and long-standing topics in computer
vision. With the recent development of deep learning techniques and large-scale datasets …

Few-shot classification aims at leveraging knowledge learned in a deep learning model, in
order to obtain good classification performance on new problems, where only a few labeled …

Few-shot learning is an important research problem that tackles one of the greatest
challenges of machine learning: learning a new task from a limited amount of labeled data …

This paper proposes a novel diffusion-based model, CompoDiff, for solving zero-shot
Composed Image Retrieval (ZS-CIR) with latent diffusion. This paper also introduces a new …

We present a new approach to modeling visual attributes. Prior work casts attributes in a
similar role as objects, learning a latent representation where properties (eg, sliced) are …

The concerns on visual privacy have been increasingly raised along with the dramatic
growth in image and video capture and sharing. Meanwhile, with the recent breakthrough in …

In this paper, we study the problem of training large-scale face identification model with
imbalanced training data. This problem naturally exists in many real scenarios including …