Image segmentation, which has become a research hotspot in the field of image processing
and computer vision, refers to the process of dividing an image into meaningful and non …

Following unprecedented success on the natural language tasks, Transformers have been
successfully applied to several computer vision problems, achieving state-of-the-art results …

Large, pretrained models are commonly finetuned with imagery that is heavily augmented to
mimic different conditions and scales, with the resulting models used for various tasks with …

It is challenging to accurately detect camouflaged objects from their highly similar
surroundings. Existing methods mainly leverage a single-stage detection fashion, while …

Consistency learning using input image, feature, or network perturbations has shown
remarkable results in semi-supervised semantic segmentation, but this approach can be …

Low-level details and high-level semantics are both essential to the semantic segmentation
task. However, to speed up the model inference, current approaches almost always sacrifice …

State-of-the-art semantic segmentation methods require sufficient labeled data to achieve
good results and hardly work on unseen classes without fine-tuning. Few-shot segmentation …

Image segmentation is a key task in computer vision and image processing with important
applications such as scene understanding, medical image analysis, robotic perception …

Object detection or localization is an incremental step in progression from coarse to fine
digital image inference. It not only provides the classes of the image objects, but also …

High-resolution representations are essential for position-sensitive vision problems, such as
human pose estimation, semantic segmentation, and object detection. Existing state-of-the …