Zero-shot remote sensing scene classification aims to solve the scene classification problem
on unseen categories and has attracted numerous research attention in the remote sensing …

The remarkable achievements of ChatGPT and Generative Pre-trained Transformer 4 (GPT-
4) have sparked a wave of interest and research in the field of large language models …

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 …

Most visual recognition studies rely heavily on crowd-labelled data in deep neural networks
(DNNs) training, and they usually train a DNN for each single visual recognition task …

We present InstructDiffusion a unified and generic framework for aligning computer vision
tasks with human instructions. Unlike existing approaches that integrate prior knowledge …

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

Pre-training across 3D vision and language remains under development because of limited
training data. Recent works attempt to transfer vision-language (VL) pre-training methods to …

Visual anomaly classification and segmentation are vital for automating industrial quality
inspection. The focus of prior research in the field has been on training custom models for …

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 …

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