As an emerging and challenging problem in the computer vision community, weakly
supervised object localization and detection plays an important role for develo** new …

Object detection or localization is an incremental step in progression from coarse to fine
digital image inference. It not only provides the classes of the image objects, but also …

Abstract Universal Image Segmentation is not a new concept. Past attempts to unify image
segmentation include scene parsing, panoptic segmentation, and, more recently, new …

Open-vocabulary segmentation is a challenging task requiring segmenting and recognizing
objects from an open set of categories in diverse environments. One way to address this …

Modern approaches typically formulate semantic segmentation as a per-pixel classification
task, while instance-level segmentation is handled with an alternative mask classification …

We present BoTNet, a conceptually simple yet powerful backbone architecture that
incorporates self-attention for multiple computer vision tasks including image classification …

Building instance segmentation models that are data-efficient and can handle rare object
categories is an important challenge in computer vision. Leveraging data augmentations is a …

This paper presents a novel cost aggregation network, called Volumetric Aggregation with
Transformers (VAT), for few-shot segmentation. The use of transformers can benefit …

Few-shot semantic segmentation aims at learning to segment a target object from a query
image using only a few annotated support images of the target class. This challenging task …

State-of-the-art semantic segmentation methods require sufficient labeled data to achieve
good results and hardly work on unseen classes without fine-tuning. Few-shot segmentation …