21 citations · 30 across the 8 of their papers we have counts for
8 papers
Logo-VGR: Visual Grounded Reasoning for Open-world Logo Recognition
Zichen Liang, Jingjing Fei, Jie Wang +6
Recent advances in multimodal large language models (MLLMs) have been primarily evaluated on general-purpose benchmarks, while their applications in domain-specific scenarios, such…
Balancing Logit Variation for Long-tailed Semantic Segmentation
Yuchao Wang, Jingjing Fei, Haochen Wang +5
Semantic segmentation usually suffers from a long-tail data distribution. Due to the imbalanced number of samples across categories, the features of those tail classes may get sque…
Pulling Target to Source: A New Perspective on Domain Adaptive Semantic Segmentation
Haochen Wang, Yujun Shen, Jingjing Fei +4
Domain adaptive semantic segmentation aims to transfer knowledge from a labeled source domain to an unlabeled target domain. However, existing methods primarily focus on directly l…
MIAD: A Maintenance Inspection Dataset for Unsupervised Anomaly Detection
Tianpeng Bao, Jiadong Chen, Wei Li +5
Visual anomaly detection plays a crucial role in not only manufacturing inspection to find defects of products during manufacturing processes, but also maintenance inspection to ke…
Learning from Future: A Novel Self-Training Framework for Semantic Segmentation
Ye Du, Yujun Shen, Haochen Wang +6
Self-training has shown great potential in semi-supervised learning. Its core idea is to use the model learned on labeled data to generate pseudo-labels for unlabeled samples, and…
Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels
Yuchao Wang, Haochen Wang, Yujun Shen +6
The crux of semi-supervised semantic segmentation is to assign adequate pseudo-labels to the pixels of unlabeled images. A common practice is to select the highly confident predict…
A Variant of Gaussian Process Dynamical Systems
Jing Zhao, Jingjing Fei, Shiliang Sun
In order to better model high-dimensional sequential data, we propose a collaborative multi-output Gaussian process dynamical system (CGPDS), which is a novel variant of GPDSs. The…
Online Anomaly Detection with Sparse Gaussian Processes
Jingjing Fei, Shiliang Sun
Online anomaly detection of time-series data is an important and challenging task in machine learning. Gaussian processes (GPs) are powerful and flexible models for modeling time-s…