284 citations · 453 across the 13 of their papers we have counts for
13 papers
KRNet: Towards Efficient Knowledge Replay
Yingying Zhang, Qiaoyong Zhong, Di Xie +1
The knowledge replay technique has been widely used in many tasks such as continual learning and continuous domain adaptation. The key lies in how to effectively encode the knowled…
Self-distilled Knowledge Delegator for Exemplar-free Class Incremental Learning
Fanfan Ye, Liang Ma, Qiaoyong Zhong +2
Exemplar-free incremental learning is extremely challenging due to inaccessibility of data from old tasks. In this paper, we attempt to exploit the knowledge encoded in a previousl…
Topology-aware Convolutional Neural Network for Efficient Skeleton-based Action Recognition
Kailin Xu, Fanfan Ye, Qiaoyong Zhong +1
In the context of skeleton-based action recognition, graph convolutional networks (GCNs) have been rapidly developed, whereas convolutional neural networks (CNNs) have received les…
Divide-and-Assemble: Learning Block-wise Memory for Unsupervised Anomaly Detection
Jinlei Hou, Yingying Zhang, Qiaoyong Zhong +3
Reconstruction-based methods play an important role in unsupervised anomaly detection in images. Ideally, we expect a perfect reconstruction for normal samples and poor reconstruct…
Modulating Localization and Classification for Harmonized Object Detection
Taiheng Zhang, Qiaoyong Zhong, Shiliang Pu +1
Object detection involves two sub-tasks, i.e. localizing objects in an image and classifying them into various categories. For existing CNN-based detectors, we notice the widesprea…
Dynamic GCN: Context-enriched Topology Learning for Skeleton-based Action Recognition
Fanfan Ye, Shiliang Pu, Qiaoyong Zhong +3
Graph Convolutional Networks (GCNs) have attracted increasing interests for the task of skeleton-based action recognition. The key lies in the design of the graph structure, which…
IROS 2019 Lifelong Robotic Vision Challenge -- Lifelong Object Recognition Report
Qi She, Fan Feng, Qi Liu +33
This report summarizes IROS 2019-Lifelong Robotic Vision Competition (Lifelong Object Recognition Challenge) with methods and results from the top finalists (out of over~…
UG Track 2: A Collective Benchmark Effort for Evaluating and Advancing Image Understanding in Poor Visibility Environments
Ye Yuan, Wenhan Yang, Wenqi Ren +3
The UG challenge in IEEE CVPR 2019 aims to evoke a comprehensive discussion and exploration about how low-level vision techniques can benefit the high-level automatic visual…
Collaborative Spatio-temporal Feature Learning for Video Action Recognition
Chao Li, Qiaoyong Zhong, Di Xie +1
Spatio-temporal feature learning is of central importance for action recognition in videos. Existing deep neural network models either learn spatial and temporal features independe…
Learning Incremental Triplet Margin for Person Re-identification
Yingying Zhang, Qiaoyong Zhong, Liang Ma +2
Person re-identification (ReID) aims to match people across multiple non-overlapping video cameras deployed at different locations. To address this challenging problem, many metric…
Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical Aggregation
Chao Li, Qiaoyong Zhong, Di Xie +1
Skeleton-based human action recognition has recently drawn increasing attentions with the availability of large-scale skeleton datasets. The most crucial factors for this task lie…
Cascade Region Proposal and Global Context for Deep Object Detection
Qiaoyong Zhong, Chao Li, Yingying Zhang +3
Deep region-based object detector consists of a region proposal step and a deep object recognition step. In this paper, we make significant improvements on both of the two steps. F…
Skeleton-based Action Recognition with Convolutional Neural Networks
Chao Li, Qiaoyong Zhong, Di Xie +1
Current state-of-the-art approaches to skeleton-based action recognition are mostly based on recurrent neural networks (RNN). In this paper, we propose a novel convolutional neural…