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 …

In the field of visual scene understanding, deep neural networks have made impressive
advancements in various core tasks like segmentation, tracking, and detection. However …

As the most fundamental scene understanding tasks, object detection and segmentation
have made tremendous progress in deep learning era. Due to the expensive manual …

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 …

CLIP, as a vision-language model, has significantly advanced Open-Vocabulary Semantic
Segmentation (OVSS) with its zero-shot capabilities. Despite its success, its application to …

Segmenting and recognizing diverse object parts is a crucial ability in applications spanning
various computer vision and robotic tasks. While significant progress has been made in …

While semantic segmentation has seen tremendous improvements in the past, there are still
significant labeling efforts necessary and the problem of limited generalization to classes …

Image-Text pretraining on web-scale image caption datasets has become the default recipe
for open vocabulary classification and retrieval models thanks to the success of CLIP and its …

In semi-supervised semantic segmentation, a model is trained with a limited number of
labeled images along with a large corpus of unlabeled images to reduce the high annotation …

Open-vocabulary semantic segmentation is a challenging task that requires segmenting
novel object categories at inference time. Recent works explore vision-language pre-training …