This article discusses the opportunities, applications and future directions of large-scale
pretrained models, ie, foundation models, which promise to significantly improve the …

Vision systems to see and reason about the compositional nature of visual scenes are
fundamental to understanding our world. The complex relations between objects and their …

Abstract Recently, Meta AI Research approaches a general, promptable segment anything
model (SAM) pre-trained on an unprecedentedly large segmentation dataset (SA-1B) …

Most polyp segmentation methods use CNNs as their backbone, leading to two key issues
when exchanging information between the encoder and decoder: 1) taking into account the …

Camouflaged object detection (COD) aims to identify the objects that conceal themselves in
natural scenes. Accurate COD suffers from a number of challenges associated with low …

This paper introduces deep gradient network (DGNet), a novel deep framework that exploits
object gradient supervision for camouflaged object detection (COD). It decouples the task …

Zero-shot point cloud segmentation aims to make deep models capable of recognizing
novel objects in point cloud that are unseen in the training phase. Recent trends favor the …

The growing threats of deepfakes to society and cybersecurity have raised enormous public
concerns, and increasing efforts have been devoted to this critical topic of deepfake video …

Concealed scene understanding (CSU) is a hot computer vision topic aiming to perceive
objects exhibiting camouflage. The current boom in terms of techniques and applications …

Abstract Point-, voxel-, and range-views are three representative forms of point clouds. All of
them have accurate 3D measurements but lack color and texture information. RGB images …