Humans can naturally and effectively find salient regions in complex scenes. Motivated by
this observation, attention mechanisms were introduced into computer vision with the aim of …

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 …

Recent research on remote sensing object detection has largely focused on improving the
representation of oriented bounding boxes but has overlooked the unique prior knowledge …

The class activation maps are generated from the final convolutional layer of CNN. They can
highlight discriminative object regions for the class of interest. These discovered object …

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

Vision transformers (ViTs) have been successfully applied in image classification tasks
recently. In this paper, we show that, unlike convolution neural networks (CNNs) that can be …

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 …

Benefiting from the capability of building inter-dependencies among channels or spatial
locations, attention mechanisms have been extensively studied and broadly used in a …

This work aims at designing a lightweight convolutional neural network for image super
resolution (SR). With simplicity bare in mind, we construct a pretty concise and effective …

Leveraging LiDAR-based detectors or real LiDAR point data to guide monocular 3D
detection has brought significant improvement, eg, Pseudo-LiDAR methods. However, the …