The emergence of large models, also known as foundation models, has brought significant
advancements to AI research. One such model is Segment Anything (SAM), which is …

We consider the generic problem of detecting low-level structures in images, which includes
segmenting the manipulated parts, identifying out-of-focus pixels, separating shadow …

The emergence of large models, also known as foundation models, has brought significant
advancements to AI research. One such model is Segment Anything (SAM), which is …

Recent deep learning methods have achieved promising results in image shadow removal.
However, their restored images still suffer from unsatisfactory boundary artifacts, due to the …

Shadow removal, which aims to restore the background in the shadow regions, is
challenging due to the highly ill-posed nature. Most existing deep learning-based methods …

Mirror detection is challenging because the visual appearances of mirrors change
depending on those of their surroundings. As existing mirror detection methods are mainly …

Powered by massive curated training data Segment Anything Model (SAM) has
demonstrated its impressive generalization capabilities in open-world scenarios with the …

Conventional image outpainting methods usually treat unobserved areas as unknown and
extend the scene only in terms of semantic consistency thus overlooking the hidden …

Existing shadow detection datasets often contain missing or mislabeled shadows, which can
hinder the performance of deep learning models trained directly on such data. To address …

Abstract We present HetNet (Multi-level Heterogeneous Network), a highly efficient mirror
detection network. Current mirror detection methods focus more on performance than …