The advent of Vision Language Models (VLMs) transformed image understanding from
closed-set classifications to dynamic image-language interactions, enabling open …

Vision-Language Models (VLMs) have emerged as promising tools for open-ended image
understanding tasks, including open vocabulary segmentation. Yet, direct application of …

Recent advancements in pre-trained vision-language models, such as CLIP, have enabled
the segmentation of arbitrary concepts solely from textual inputs, a process commonly …

Recent success of pre-trained foundation vision-language models makes Open-Vocabulary
Segmentation (OVS) possible. Despite the promising performance this approach introduces …

From image-text pairs large-scale vision-language models (VLMs) learn to implicitly
associate image regions with words which prove effective for tasks like visual question …

From an enormous amount of image-text pairs, large-scale vision-language models (VLMs)
learn to implicitly associate image regions with words, which is vital for tasks such as image …

This paper studies open-vocabulary segmentation (OVS) through calibrating in-vocabulary
and domain-biased embedding space with generalized contextual prior of CLIP. As the core …

We design an open-vocabulary image segmentation model to organize an image into
meaningful regions indicated by arbitrary texts. Recent works (CLIP and ALIGN), despite …

Recently, open-vocabulary image classification by vision language pre-training has
demonstrated incredible achievements, that the model can classify arbitrary categories …

Semantic segmentation is a crucial task in computer vision, where each pixel in an image is
classified into a category. However, traditional methods face significant challenges …