Object detection as a subfield within computer vision has achieved remarkable progress,
which aims to accurately identify and locate a specific object from images or videos. Such …

Few-shot learning (FSL) is popular due to its ability to adapt to novel classes. Compared
with inductive few-shot learning, transductive models typically perform better as they …

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

Recent advancements in few-shot segmentation (FSS) have exploited pixel-by-pixel
matching between query and support features, typically based on cross attention, which …

This paper proposes a new active learning method for semantic segmentation. The core of
our method lies in a new annotation query design. It samples informative local image …

In this work, we revisit the prior mask guidance proposed in “Prior Guided Feature
Enrichment Network for Few-Shot Segmentation”. The prior mask serves as an indicator that …

Traditional approaches for learning on categorical data underexploit the dependencies
between columns (aka fields) in a dataset because they rely on the embedding of data …

Image segmentation is a long-standing challenge in computer vision, studied continuously
over several decades, as evidenced by seminal algorithms such as N-Cut, FCN, and …

The goal of few-shot segmentation (FSS) is to segment unlabeled images belonging to
previously unseen classes using only a limited number of labeled images. The main …

Adversarially robust knowledge distillation aims to compress large-scale models into
lightweight models while preserving adversarial robustness and natural performance on a …