Since experiencing domain shifts during test-time is inevitable in practice, test-time adaption
(TTA) continues to adapt the model after deployment. Recently, the area of continual and …

Since distribution shifts are likely to occur during test-time and can drastically decrease the
model's performance, online test-time adaptation (TTA) continues to update the model after …

Accurate monitoring of bare soil land (BSL) is an urgent need for environmental governance
and optimal utilization of land resources. High-resolution imagery contains rich semantic …

In the present study, an integrated framework for automatic detection, segmentation, and
measurement of road surface cracks is proposed. First, road images are captured, and crack …

In this paper we address the challenge of selecting an optimal dataset from a source pool
with annotations to enhance performance on a target dataset derived from a different source …

The generalization ability of the fetal head segmentation method is reduced due to the data
obtained by different machines, settings, and operations. To keep the generalization ability …

Thanks to its great power in feature representation, deep learning (DL) is widely used in
semantic segmentation tasks. However, the requirements for high distribution consistency of …

Deep learning models dealing with image understanding in real-world settings must be able
to adapt to a wide variety of tasks across different domains. Domain adaptation and class …

Experiencing domain shifts during test-time is nearly inevitable in practice and likely results
in a severe performance degradation. To overcome this issue, test-time adaptation …

We proposed an easy method of Zero-Shot semantic segmentation by using style transfer. In
this case, we successfully used a medical imaging dataset (Blood Cell Imagery) to train a …