Visual segmentation seeks to partition images, video frames, or point clouds into multiple
segments or groups. This technique has numerous real-world applications, such as …

We propose a method to efficiently equip the Segment Anything Model (SAM) with the ability
to generate regional captions. SAM presents strong generalizability to segment anything …

Recent advances in contrastive language-image pretraining (CLIP) have demonstrated
strong capabilities in zero-shot classification by aligning visual representations with target …

Recently, methods have been proposed for 3D open-vocabulary semantic segmentation.
Such methods are able to segment scenes into arbitrary classes based on text descriptions …

The joint classification of hyperspectral images (HSIs) and LiDAR data plays a crucial role in
Earth observation missions. Most advanced methods are based on discrete label …

This paper addresses text-supervised semantic segmentation aiming to learn a model
capable of segmenting arbitrary visual concepts within images by using only image-text …

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

Open-vocabulary semantic segmentation (OVS) aims to segment images of arbitrary
categories specified by class labels or captions. However most previous best-performing …

The crux of learning vision-language models is to extract semantically aligned information
from visual and linguistic data. Existing attempts usually face the problem of coarse …

The contrastive language-image pretraining (CLIP) model has shown promise in
generalization recognition by combining visual and textual embeddings. However, the …