Open-vocabulary semantic segmentation aims to segment an image into semantic regions
according to text descriptions, which may not have been seen during training. Recent two …

Open-vocabulary semantic segmentation presents the challenge of labeling each pixel
within an image based on a wide range of text descriptions. In this work we introduce a …

Recently, the open-vocabulary semantic segmentation problem has attracted increasing
attention and the best performing methods are based on two-stream networks: one stream …

Open-vocabulary semantic segmentation strives to distinguish pixels into different semantic
groups from an open set of categories. Most existing methods explore utilizing pre-trained …

This paper presents a new framework for open-vocabulary semantic segmentation with the
pre-trained vision-language model, named SAN. Our approach models the semantic …

In this paper, we consider the problem of open-vocabulary semantic segmentation (OVS),
which aims to segment objects of arbitrary classes instead of pre-defined, closed-set …

Recently, the contrastive language-image pre-training, eg, CLIP, has demonstrated
promising results on various downstream tasks. The pre-trained model can capture enriched …

In this paper, we tackle an emerging computer vision task, open-vocabulary universal image
segmentation, that aims to perform semantic/instance/panoptic segmentation (background …

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 …

This article concentrates on open-vocabulary semantic segmentation, where a well
optimized model is able to segment arbitrary categories that appear in an image. To achieve …