To solve object detection issues in infrared images, such as a low recognition rate and a
high false alarm rate caused by long distances, weak energy, and low resolution, we …

Neural network based computer vision systems are typically built on a backbone, a
pretrained or randomly initialized feature extractor. Several years ago, the default option was …

Transformers have quickly shined in the computer vision world since the emergence of
Vision Transformers (ViTs). The dominant role of convolutional neural networks (CNNs) …

The past year has witnessed the rapid development of applying the Transformer module to
vision problems. While some researchers have demonstrated that Transformer-based …

This paper presents a new vision Transformer, Scale Aware Modulation Transformer (SMT),
that can handle various downstream tasks efficiently by combining the convolutional network …

End-to-end scene text spotting has attracted great attention in recent years due to the
success of excavating the intrinsic synergy of the scene text detection and recognition …

Detection transformer (DETR) relies on one-to-one assignment, assigning one ground-truth
object to one prediction, for end-to-end detection without NMS post-processing. It is known …

Complicated underwater environments bring new challenges to object detection, such as
unbalanced light conditions, low contrast, occlusion, and mimicry of aquatic organisms …

Recent transformer-based models, especially patch-based methods, have shown huge
potentiality in vision tasks. However, the split fixed-size patches divide the input features into …

Abstract Visual Transformers (VTs) are emerging as an architectural paradigm alternative to
Convolutional networks (CNNs). Differently from CNNs, VTs can capture global relations …