Given the large volume of remote sensing images collected daily, automatic object detection
and segmentation have been a consistent need in Earth observation (EO). However, objects …

Visual segmentation seeks to partition images, video frames, or point clouds into multiple
segments or groups. This technique has numerous real-world applications, such as …

Transformers have recently emerged as a powerful tool for learning visual representations.
In this paper, we identify and characterize artifacts in feature maps of both supervised and …

Unsupervised multi-object segmentation has shown impressive results on images by
utilizing powerful semantics learned from self-supervised pretraining. An additional modality …

Referring image segmentation (RIS) aims to find a segmentation mask given a referring
expression grounded to a region of the input image. Collecting labelled datasets for this …

Open-vocabulary semantic segmentation requires models to effectively integrate visual
representations with open-vocabulary semantic labels. While Contrastive Language-Image …

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 …

Several unsupervised image segmentation approaches have been proposed which
eliminate the need for dense manually-annotated segmentation masks; current models …

Spatially dense self-supervised learning is a rapidly growing problem domain with
promising applications for unsupervised segmentation and pretraining for dense …

The recent success of transformer-based image generative models in object-centric learning
highlights the importance of powerful image generators for handling complex scenes …