57 citations · 99 across the 7 of their papers we have counts for
7 papers
Prompt-Matched Semantic Segmentation
Lingbo Liu, Jianlong Chang, Bruce X. B. Yu +3
The objective of this work is to explore how to effectively and efficiently adapt pre-trained visual foundation models to various downstream tasks of semantic segmentation. Previou…
Semi-supervised Object Detection via Virtual Category Learning
Changrui Chen, Kurt Debattista, Jungong Han
Due to the costliness of labelled data in real-world applications, semi-supervised object detectors, underpinned by pseudo labelling, are appealing. However, handling confusing sam…
Learning to Aggregate Multi-Scale Context for Instance Segmentation in Remote Sensing Images
Ye Liu, Huifang Li, Chao Hu +3
The task of instance segmentation in remote sensing images, aiming at performing per-pixel labeling of objects at instance level, is of great importance for various civil applicati…
Gaussian Dynamic Convolution for Efficient Single-Image Segmentation
Xin Sun, Changrui Chen, Xiaorui Wang +3
Interactive single-image segmentation is ubiquitous in the scientific and commercial imaging software. In this work, we focus on the single-image segmentation problem only with som…
High-Order Paired-ASPP Networks for Semantic Segmenation
Yu Zhang, Xin Sun, Junyu Dong +2
Current semantic segmentation models only exploit first-order statistics, while rarely exploring high-order statistics. However, common first-order statistics are insufficient to s…
Highlight Every Step: Knowledge Distillation via Collaborative Teaching
Haoran Zhao, Xin Sun, Junyu Dong +2
High storage and computational costs obstruct deep neural networks to be deployed on resource-constrained devices. Knowledge distillation aims to train a compact student network by…
Few-shot Learning for Domain-specific Fine-grained Image Classification
Xin Sun, Hongwei Xv, Junyu Dong +2
Learning to recognize novel visual categories from a few examples is a challenging task for machines in real-world industrial applications. In contrast, humans have the ability to…