Edge detection is one of the most important and fundamental problems in the field of
computer vision and image processing. Edge contours extracted from images are widely …

Abstract We introduce the Segment Anything (SA) project: a new task, model, and dataset for
image segmentation. Using our efficient model in a data collection loop, we built the largest …

The recently proposed segment anything model (SAM) has made a significant influence in
many computer vision tasks. It is becoming a foundation step for many high-level tasks, like …

This paper proposes a new framework for low-light image enhancement by simultaneously
conducting the appearance as well as structure modeling. It employs the structural feature to …

We provide a two-way integration for the widely-adopted ControlNet by integrating external
condition generation algorithms into a single dense prediction method and by integrating its …

Deep learning-based edge detectors heavily rely on pixel-wise labels which are often
provided by multiple annotators. Existing methods fuse multiple annotations using a simple …

Edge detection is the basis of many computer vision applications. State of the art
predominantly relies on deep learning with two decisive factors: dataset content and network …

Edge detection is represented as one of the most challenging tasks in computer vision, due
to the complexity of detecting the edges or boundaries in real-world images that contains …

Limited by the encoder-decoder architecture, learning-based edge detectors usually have
difficulty predicting edge maps that satisfy both correctness and crispness. With the recent …

Edge segmentation is well-known to be subjective due to personalized annotation styles
and preferred granularity. However most existing deterministic edge detection methods …