The loss function, also known as cost function, is used for training a neural network or other
machine learning models. Over the past decade, researchers have designed many loss …

Transformers have demonstrated impressive expressiveness and transfer capability in
computer vision fields. Dense prediction is a fundamental problem in computer vision that is …

Transformers have recently emerged as a powerful tool for learning visual representations.
In this paper, we identify and characterize artifacts in feature maps of both supervised and …

Combining simple architectures with large-scale pre-training has led to massive
improvements in image classification. For object detection, pre-training and scaling …

The visual feature pyramid has shown its superiority in both effectiveness and efficiency in a
variety of applications. However, current methods overly focus on inter-layer feature …

Abstract Within Convolutional Neural Network (CNN), the convolution operations are good
at extracting local features but experience difficulty to capture global representations. Within …

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 …

Current state-of-the-art methods for image captioning employ region-based features, as they
provide object-level information that is essential to describe the content of images; they are …

In this paper we introduce the Temporo-Spatial Vision Transformer (TSViT), a fully-
attentional model for general Satellite Image Time Series (SITS) processing based on the …

Object localization in general environments is a fundamental part of vision systems. While
dominating on the COCO benchmark, recent Transformer-based detection methods are not …