Deep neural models, in recent years, have been successful in almost every field, even
solving the most complex problem statements. However, these models are huge in size with …

In this paper, we explore a novel and ambitious knowledge-transfer task, termed Knowledge
Factorization (KF). The core idea of KF lies in the modularization and assemblability of …

Despite the visually-pleasing results achieved, the massive computational cost has been a
long-standing flaw for diffusion probabilistic models (DPMs), which, in turn, greatly limits …

摘要高性能的深度学习网络通常是计算型和参数密集型的, 难以应用于资源受限的边缘设备.
为了能够在低资源设备上运行深度学习模型, 需要研发高效的小规模网络 …

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 …

Most existing distillation methods ignore the flexible role of the temperature in the loss
function and fix it as a hyper-parameter that can be decided by an inefficient grid search. In …

In recent years, various neural network architectures for computer vision have been devised,
such as the visual transformer and multilayer perceptron (MLP). A transformer based on an …

Existing knowledge distillation methods focus on convolutional neural networks (CNNs),
where the input samples like images lie in a grid domain, and have largely overlooked …

Existing state-of-the-art human pose estimation methods require heavy computational
resources for accurate predictions. One promising technique to obtain an accurate yet …

Cloth-Changing person re-identification (CC-ReID) aims at matching the same person
across different locations over a long-duration, eg, over days, and therefore inevitably has …