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 …

Semantic segmentation has a broad range of applications, but its real-world impact has
been significantly limited by the prohibitive annotation costs necessary to enable …

In this paper, we describe a graph-based algorithm that uses the features obtained by a self-
supervised transformer to detect and segment salient objects in images and videos. With this …

The variety of objects in the real world is nearly unlimited and is thus impossible to capture
using models trained on a fixed set of categories. As a result, in recent years, open …

Recent advances in self-supervised visual representation learning have paved the way for
unsupervised methods tackling tasks such as object discovery and instance segmentation …

When trained at a sufficient scale, self-supervised learning has exhibited a notable ability to
solve a wide range of visual or language understanding tasks. In this paper, we investigate …

Abstract Deep Learning-based Unsupervised Salient Object Detection (USOD) mainly relies
on the noisy saliency pseudo labels that have been generated from traditional handcraft …

The paper introduces PaintSeg, a new unsupervised method for segmenting objects without
any training. We propose an adversarial masked contrastive painting (AMCP) process …

Abstract We present Lazy Visual Grounding for open-vocabulary semantic segmentation,
which decouples unsupervised object mask discovery from object grounding. Plenty of the …

Motion, measured via optical flow, provides a powerful cue to discover and learn objects in
images and videos. However, compared to using appearance, it has some blind spots, such …