Early detection and assessment of polyps play a crucial role in the prevention and treatment
of colorectal cancer (CRC). Polyp segmentation provides an effective solution to assist …

The computational complexity of the self-attention mechanism in popular transformer
architectures poses significant challenges for training and inference, and becomes the …

Background Neuroimaging segmentation is increasingly important for diagnosing and
planning treatments for neurological diseases. Manual segmentation is time-consuming …

Employing deep learning techniques for the semantic segmentation of remote sensing
images has emerged as a prevalent approach for acquiring information about water bodies …

Skin cancer, a growing health concern, poses a significant threat. Automated segmentation
of skin lesions is crucial for precise clinical diagnosis and treatment. However, challenges …

Abstract In recent years, Neural Architecture Search (NAS) has received widespread
attention for its remarkable ability to design neural networks. However, existing NAS works …

This survey provides a comprehensive evaluation of various deep learning-based
segmentation architectures. It covers a wide range of models, from traditional ones like FCN …

Recent studies have demonstrated the significant importance of two factors: global context
and multi-level semantics, for semantic segmentation models. However, obtaining features …

Recently, the emergence of the large-scale vision-language model (VLM), such as CLIP,
has opened the way towards open-world object perception. Many works have explored the …

Abstract Street View Imagery (SVI) is a valuable data source for studies (eg, environmental
assessments, green space identification or land cover classification). While commercial SVI …