Wanli Ouyang

The University of Sydney
0000-0002-9163-2761
wlouyang.github.io
Publications231
h-index67
Citations20,397
Highly Influential Citations2,008
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  • Publications
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Unsupervised Salience Learning for Person Re-identification
TLDR
A novel perspective for person re-identification based on unsupervised salience learning, which applies adjacency constrained patch matching to build dense correspondence between image pairs, which shows effectiveness in handling misalignment caused by large viewpoint and pose variations.
Hybrid Task Cascade for Instance Segmentation
TLDR
This work proposes a new framework, Hybrid Task Cascade (HTC), which differs in two important aspects: (1) instead of performing cascaded refinement on these two tasks separately, it interweaves them for a joint multi-stage processing; (2) it adopts a fully convolutional branch to provide spatial context, which can help distinguishing hard foreground from cluttered background.
Saliency detection by multi-context deep learning
TLDR
This paper proposes a multi-context deep learning framework for salient object detection that employs deep Convolutional Neural Networks to model saliency of objects in images and investigates different pre-training strategies to provide a better initialization for training the deep neural networks.
Visual Tracking with Fully Convolutional Networks
TLDR
An in-depth study on the properties of CNN features offline pre-trained on massive image data and classification task on ImageNet shows that the proposed tacker outperforms the state-of-the-art significantly.
Learning Deep Feature Representations with Domain Guided Dropout for Person Re-identification
TLDR
This work presents a pipeline for learning deep feature representations from multiple domains with Convolutional Neural Networks with CNNs and proposes a Domain Guided Dropout algorithm to improve the feature learning procedure.
Libra R-CNN: Towards Balanced Learning for Object Detection
TLDR
Libra R-CNN is proposed, a simple but effective framework towards balanced learning for object detection that integrates three novel components: IoU-balanced sampling, balanced feature pyramid, and balanced L1 loss, respectively for reducing the imbalance at sample, feature, and objective level.
MMDetection: Open MMLab Detection Toolbox and Benchmark
TLDR
This paper presents MMDetection, an object detection toolbox that contains a rich set of object detection and instance segmentation methods as well as related components and modules, and conducts a benchmarking study on different methods, components, and their hyper-parameters.
Learning Mid-level Filters for Person Re-identification
TLDR
This paper proposes a novel approach of learning mid-level filters from automatically discovered patch clusters for person re-identification that is complementary to existing handcrafted low-level features, and improves the best Rank-1 matching rate on the VIPeR dataset by 14%.
Person Re-identification by Salience Matching
TLDR
This paper exploits the pair wise salience distribution relationship between pedestrian images, and solves the person re-identification problem by proposing a salience matching strategy that outperforms the state-of-the-art methods on both datasets.
Exploit the Unknown Gradually: One-Shot Video-Based Person Re-identification by Stepwise Learning
TLDR
This paper proposes an approach to exploiting unlabeled tracklets by gradually but steadily improving the discriminative capability of the Convolutional Neural Network feature representation via stepwise learning.
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