The machine learning community has been overwhelmed by a plethora of deep learning--
based approaches. Many challenging computer vision tasks, such as detection, localization …

Semantic image segmentation, which becomes one of the key applications in image
processing and computer vision domain, has been used in multiple domains such as …

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 …

We present the first systematic study on concealed object detection (COD), which aims to
identify objects that are visually embedded in their background. The high intrinsic similarities …

We study the use of deep features extracted from a pretrained Vision Transformer (ViT) as
dense visual descriptors. We observe and empirically demonstrate that such features, when …

There is a tendency across different subfields in AI to valorize a small collection of influential
benchmarks. These benchmarks operate as stand-ins for a range of anointed common …

We present a comprehensive study on a new task named camouflaged object detection
(COD), which aims to identify objects that are" seamlessly" embedded in their surroundings …

We present a new method for open-vocabulary universal image segmentation, which is
capable of performing instance, semantic, and panoptic segmentation under a unified …

Although it is well believed for years that modeling relations between objects would help
object recognition, there has not been evidence that the idea is working in the deep learning …

The fundamental mathematical tools needed to understand machine learning include linear
algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability …