18 citations · 31 across the 8 of their papers we have counts for
8 papers
Recolour What Matters: Region-Aware Colour Editing via Token-Level Diffusion
Yuqi Yang, Dongliang Chang, Yijia Ling +2
Colour is one of the most perceptually salient yet least controllable attributes in image generation. Although recent diffusion models can modify object colours from user instructi…
Controllable-Continuous Color Editing in Diffusion Model via Color Mapping
Yuqi Yang, Dongliang Chang, Yuanchen Fang +3
In recent years, text-driven image editing has made significant progress. However, due to the inherent ambiguity and discreteness of natural language, color editing still faces cha…
Empowering Segmentation Ability to Multi-modal Large Language Models
Yuqi Yang, Peng-Tao Jiang, Jing Wang +4
Multi-modal large language models (MLLMs) can understand image-language prompts and demonstrate impressive reasoning ability. In this paper, we extend MLLMs' output by empowering M…
Multi-Task Dense Prediction via Mixture of Low-Rank Experts
Yuqi Yang, Peng-Tao Jiang, Qibin Hou +3
Previous multi-task dense prediction methods based on the Mixture of Experts (MoE) have received great performance but they neglect the importance of explicitly modeling the global…
CorrMatch: Label Propagation via Correlation Matching for Semi-Supervised Semantic Segmentation
Boyuan Sun, Yuqi Yang, Le Zhang +2
This paper presents a simple but performant semi-supervised semantic segmentation approach, called CorrMatch. Previous approaches mostly employ complicated training strategies to l…
Segment Anything is A Good Pseudo-label Generator for Weakly Supervised Semantic Segmentation
Peng-Tao Jiang, Yuqi Yang
Weakly supervised semantic segmentation with weak labels is a long-lived ill-posed problem. Mainstream methods mainly focus on improving the quality of pseudo labels. In this repor…
Traffic Scene Parsing through the TSP6K Dataset
Peng-Tao Jiang, Yuqi Yang, Yang Cao +3
Traffic scene perception in computer vision is a critically important task to achieve intelligent cities. To date, most existing datasets focus on autonomous driving scenes. We obs…
L2G: A Simple Local-to-Global Knowledge Transfer Framework for Weakly Supervised Semantic Segmentation
Peng-Tao Jiang, Yuqi Yang, Qibin Hou +1
Mining precise class-aware attention maps, a.k.a, class activation maps, is essential for weakly supervised semantic segmentation. In this paper, we present L2G, a simple online lo…