In computer vision, significant advances have been made on object detection with the rapid
development of deep convolutional neural networks (CNN). This paper provides a …

Object detection is a fundamental visual recognition problem in computer vision and has
been widely studied in the past decades. Visual object detection aims to find objects of …

Existing low-light image enhancement techniques are mostly not only difficult to deal with
both visual quality and computational efficiency but also commonly invalid in unknown …

With the rise of deep convolutional neural networks, object detection has achieved
prominent advances in past years. However, such prosperity could not camouflage the …

Feature fusion, the combination of features from different layers or branches, is an
omnipresent part of modern network architectures. It is often implemented via simple …

Generating expressive talking heads is essential for creating virtual humans. However,
existing one-or few-shot methods focus on lip-sync and head motion, ignoring the emotional …

Detecting tiny objects is one of the main obstacles hindering the development of object
detection. The performance of generic object detectors tends to drastically deteriorate on tiny …

To mitigate the issue of minimal intrinsic features for pure data-driven methods, in this article,
we propose a novel model-driven deep network for infrared small target detection, which …

Tiny deep learning on microcontroller units (MCUs) is challenging due to the limited memory
size. We find that the memory bottleneck is due to the imbalanced memory distribution in …

Arbitrary-oriented objects widely appear in natural scenes, aerial photographs, remote
sensing images, etc., and thus arbitrary-oriented object detection has received considerable …