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 …

Deep learning, which is a subfield of machine learning, has opened a new era for the
development of neural networks. The auto-encoder is a key component of deep structure …

Decoding visual stimuli from brain recordings aims to deepen our understanding of the
human visual system and build a solid foundation for bridging human and computer vision …

Deep learning's recent history has been one of achievement: from triumphing over humans
in the game of Go to world-leading performance in image classification, voice recognition …

We present a simple, fully-convolutional model for real-time instance segmentation that
achieves 29.8 mAP on MS COCO at 33.5 fps evaluated on a single Titan Xp, which is …

In crowd counting datasets, each person is annotated by a point, which is usually the center
of the head. And the task is to estimate the total count in a crowd scene. Most of the state-of …

Abstract Convolutional Neural Networks (CNNs) use pooling to decrease the size of
activation maps. This process is crucial to increase the receptive fields and to reduce …

This paper focuses on the emerging Infrared-Visible cross-modal person re-identification
task (IV-ReID), which takes infrared images as input and matches with visible color images …

Object recognition is a key research area in the field of image processing and computer
vision, which recognizes the object in an image and provides a proper label. In the paper …

Remote sensing image scene classification plays an important role in a wide range of
applications and hence has been receiving remarkable attention. During the past years …