Camouflaged object detection (COD) is the challenging task of identifying camouflaged
objects visually blended into surroundings. Albeit achieving remarkable success, existing …

Abstract Masked Image Modeling (MIM) achieves outstanding success in self-supervised
representation learning. Unfortunately, MIM models typically have huge computational …

Data augmentation is widely known as a simple yet surprisingly effective technique for
regularizing deep networks. Conventional data augmentation schemes, eg, flipping …

In this paper, we explore the spatial redundancy in video recognition with the aim to improve
the computational efficiency. It is observed that the most informative region in each frame of …

This paper proposes a novel batch normalization strategy for test-time adaptation. Recent
test-time adaptation methods heavily rely on the modified batch normalization, ie …

Abstract Knowledge distillation is an effective approach to learn compact models (students)
with the supervision of large and strong models (teachers). As empirically there exists a …

We propose a new neural network design paradigm Reversible Column Network (RevCol).
The main body of RevCol is composed of multiple copies of subnetworks, named columns …

The recent detection transformer (DETR) has advanced object detection, but its application
on resource-constrained devices requires massive computation and memory resources …

The superior performance of modern deep networks usually comes with a costly training
procedure. This paper presents a new curriculum learning approach for the efficient training …

Recently, the enactment of privacy regulations has promoted the rise of the machine
unlearning paradigm. Existing studies of machine unlearning mainly focus on sample-wise …