Document images are often degraded by various stains, significantly impacting their
readability and hindering downstream applications such as document digitization and …

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 …

Machine learning algorithms have achieved remarkable success across various disciplines,
use cases and applications, under the prevailing assumption that training and test samples …

Medical landmark detection is crucial in various medical imaging modalities and
procedures. Although deep learning-based methods have achieve promising performance …

Social media is increasingly plagued by realistic fake images, making it hard to trust content.
Previous algorithms to detect these fakes often fail in new, real-world scenarios because …

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 …

Existing zero-shot temporal action detection (ZSTAD) methods predominantly use fully
supervised or unsupervised strategies to recognize unseen activities. However, these …

In the field of computer vision, visible light images often exhibit low contrast in low-light
conditions, presenting a significant challenge. While infrared imagery provides a potential …

Shadows in scanned documents pose significant challenges for document analysis and
recognition tasks due to their negative impact on visual quality and readability. Current …

Benefiting from large-scale pre-training of text-video pairs, current text-to-video (T2V)
diffusion models can generate high-quality videos from the text description. Besides, given …