activity
20192021
most citedIncremental Few-Shot Learning for Pedestrian Attribute Recognition

5 citations · 7 across the 3 of their papers we have counts for

collaborators

5 papers

cs.CV20211 cited

LODE: Deep Local Deblurring and A New Benchmark

Zerun Wang, Liuyu Xiang, Fan Yang +6

While recent deep deblurring algorithms have achieved remarkable progress, most existing methods focus on the global deblurring problem, where the image blur mostly arises from sev…

cs.CV2020

PANDA: A Gigapixel-level Human-centric Video Dataset

Xueyang Wang, Xiya Zhang, Yinheng Zhu +8

We present PANDA, the first gigaPixel-level humAN-centric viDeo dAtaset, for large-scale, long-term, and multi-object visual analysis. The videos in PANDA were captured by a gigapi…

cs.CV2020

Learning From Multiple Experts: Self-paced Knowledge Distillation for Long-tailed Classification

Liuyu Xiang, Guiguang Ding, Jungong Han

In real-world scenarios, data tends to exhibit a long-tailed distribution, which increases the difficulty of training deep networks. In this paper, we propose a novel self-paced kn…

cs.CV20195 cited

Incremental Few-Shot Learning for Pedestrian Attribute Recognition

Liuyu Xiang, Xiaoming Jin, Guiguang Ding +2

Pedestrian attribute recognition has received increasing attention due to its important role in video surveillance applications. However, most existing methods are designed for a f…

cs.CL20191 cited

Adaptive Region Embedding for Text Classification

Liuyu Xiang, Xiaoming Jin, Lan Yi +1

Deep learning models such as convolutional neural networks and recurrent networks are widely applied in text classification. In spite of their great success, most deep learning mod…