Image classification has always been a hot research direction in the world, and the
emergence of deep learning has promoted the development of this field. Convolutional …

Object Detection is the task of classification and localization of objects in an image or video.
It has gained prominence in recent years due to its widespread applications. This article …

In this paper, we explore a novel knowledge-transfer task, termed as Deep Model
Reassembly (DeRy), for general-purpose model reuse. Given a collection of heterogeneous …

In this paper, we propose a conceptually simple but very effective attention module for
Convolutional Neural Networks (ConvNets). In contrast to existing channel-wise and spatial …

Compute, data, and algorithmic advances are the three fundamental factors that drive
progress in modern Machine Learning (ML). In this paper we study trends in the most readily …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

Recent studies on mobile network design have demonstrated the remarkable effectiveness
of channel attention (eg, the Squeeze-and-Excitation attention) for lifting model performance …

We present a simple but powerful architecture of convolutional neural network, which has a
VGG-like inference-time body composed of nothing but a stack of 3x3 convolution and …

Low-light image enhancement (LLIE) aims at improving the perception or interpretability of
an image captured in an environment with poor illumination. Recent advances in this area …

Multi-task learning can leverage information learned by one task to benefit the training of
other tasks. Despite this capacity, naively training all tasks together in one model often …