To ensure good performance, modern machine learning models typically require large
amounts of quality annotated data. Meanwhile, the data collection and annotation processes …

Many image processing, computer graphics, and computer vision problems can be treated
as image-to-image translation tasks. Such translation entails learning to map one visual …

Score-based diffusion models (SBDMs) have achieved the SOTA FID results in unpaired
image-to-image translation (I2I). However, we notice that existing methods totally ignore the …

Image-to-image translation (I2I) aims to transfer images from a source domain to a target
domain while preserving the content representations. I2I has drawn increasing attention and …

We present a novel Transformer-based network architecture for instance-aware image-to-
image translation, dubbed InstaFormer, to effectively integrate global-and instance-level …

The area of domain adaptation has been instrumental in addressing the domain shift
problem encountered by many applications. This problem arises due to the difference …

Convolutional neural networks (CNN) are capable of learning robust representation with
different regularization methods and activations as convolutional layers are spatially …

Recently, convolutional neural networks (CNNs) have achieved state-of-the-art performance
in infrared small target detection. However, the limited number of public training data …

Unsupervised domain adaptive object detection aims to learn a robust detector in the
domain shift circumstance, where the training (source) domain is label-rich with bounding …

Image colorization is inherently an ill-posed problem with multi-modal uncertainty. Previous
methods leverage the deep neural network to map input grayscale images to plausible color …