Specular highlight removal plays a pivotal role in multimedia applications, as it enhances
the quality and interpretability of images and videos, ultimately improving the performance of …

The ability to translate medical images across different modalities is crucial for synthesizing
missing data and aiding in clinical diagnosis. However, existing learning-based techniques …

Objective: Histological classification is a challenging task due to the diverse appearances,
unpredictable variations, and blurry edges of histological tissues. Recently, many …

Can we obtain insights about the brain using AI models? How is the information in deep
learning models related to brain recordings? Can we improve AI models with the help of …

In this work, we introduce DOPRA, a novel approach designed to mitigate hallucinations in
multi-modal large language models (MLLMs). Unlike existing solutions that typically involve …

Abstract Weakly Supervised Semantic Segmentation (WSSS) has become increasingly
popular for achieving remarkable segmentation with only image-level labels. Current WSSS …

Shadow detection is crucial for accurate scene understanding in computer vision, yet it is
challenged by the diverse appearances of shadows caused by variations in illumination …

Underwater images often exhibit poor quality, distorted color balance and low contrast due
to the complex and intricate interplay of light, water, and objects. Despite the significant …

Underwater image enhancement (UIE) plays a crucial role in various marine applications,
but it remains challenging due to the complex underwater environment. Current learning …

Accurate segmentation of lesion regions is crucial for clinical diagnosis and treatment across
various diseases. While deep convolutional networks have achieved satisfactory results in …