Remote sensing image scene classification, which aims at labeling remote sensing images
with a set of semantic categories based on their contents, has broad applications in a range …

With the rapid development of deep learning techniques, deep convolutional neural
networks (DCNNs) have become more important for object detection. Compared with …

Video understanding requires reasoning at multiple spatiotemporal resolutions--from short
fine-grained motions to events taking place over longer durations. Although transformer …

Large, pretrained models are commonly finetuned with imagery that is heavily augmented to
mimic different conditions and scales, with the resulting models used for various tasks with …

Fine-grained image analysis (FGIA) is a longstanding and fundamental problem in computer
vision and pattern recognition, and underpins a diverse set of real-world applications. The …

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 …

There are a huge number of features which are said to improve Convolutional Neural
Network (CNN) accuracy. Practical testing of combinations of such features on large …

Spatial pooling has been proven highly effective to capture long-range contextual
information for pixel-wise prediction tasks, such as scene parsing. In this paper, beyond …

Recently, the vision transformer has achieved great success by pushing the state-of-the-art
of various vision tasks. One of the most challenging problems in the vision transformer is that …

Computer-aided diagnosis (CAD) methods such as Chest X-rays (CXR)-based method is
one of the cheapest alternative options to diagnose the early stage of COVID-19 disease …