In the last few years, deep learning has led to very good performance on a variety of
problems, such as visual recognition, speech recognition and natural language processing …

Large deep learning models are impressive, but they struggle when real-time data is not
available. Few-shot class-incremental learning (FSCIL) poses a significant challenge for …

Federated Learning (FL) is an emerging distributed learning paradigm under privacy
constraint. Data heterogeneity is one of the main challenges in FL, which results in slow …

Making generative models 3D-aware bridges the 2D image space and the 3D physical
world yet remains challenging. Recent attempts equip a Generative Adversarial Network …

Vehicle re-identification (Re-ID) is one of the promising applications in the field of computer
vision. Existing vehicle Re-ID methods mainly focus on global appearance features or pre …

Abstract 3D-aware generative models have shown that the introduction of 3D information
can lead to more controllable image generation. In particular, the current state-of-the-art …

Abstract We present Text2Tex, a novel method for generating high-quality textures for 3D
meshes from the given text prompts. Our method incorporates inpainting into a pre-trained …

We propose a novel generative adversarial network (GAN) for the task of unsupervised
learning of 3D representations from natural images. Most generative models rely on 2D …

Delicate feature representation about object parts plays a critical role in fine-grained
recognition. For example, experts can even distinguish fine-grained objects relying only on …

Visual explanation enables humans to understand the decision making of deep
convolutional neural network (CNN), but it is insufficient to contribute to improving CNN …