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Honghui Shi

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4 papers

1 author row

AAAI Conference 2020 Conference Paper

When AWGN-Based Denoiser Meets Real Noises

Yuqian Zhou
Jianbo Jiao
Haibin Huang
Yang Wang
Jue Wang
Honghui Shi
Thomas Huang

Discriminative learning based image denoisers have achieved promising performance on synthetic noises such as Additive White Gaussian Noise (AWGN). The synthetic noises adopted in most previous work are pixel-independent, but real noises are mostly spatially/channel-correlated and spatially/channel-variant. This domain gap yields unsatisﬁed performance on images with real noises if the model is only trained with AWGN. In this paper, we propose a novel approach to boost the performance of a real image denoiser which is trained only with synthetic pixel-independent noise data dominated by AWGN. First, we train a deep model that consists of a noise estimator and a denoiser with mixed AWGN and Random Value Impulse Noise (RVIN). We then investigate Pixel-shufﬂe Down-sampling (PD) strategy to adapt the trained model to real noises. Extensive experiments demonstrate the effectiveness and generalization of the proposed approach. Notably, our method achieves state-of-theart performance on real sRGB images in the DND benchmark among models trained with synthetic noises. Codes are available at https: //github. com/yzhouas/PD-Denoising-pytorch.

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AAAI Conference 2019 Conference Paper

Horizontal Pyramid Matching for Person Re-Identification

Yang Fu
Yunchao Wei
Yuqian Zhou
Honghui Shi
Gao Huang
Xinchao Wang
Zhiqiang Yao
Thomas Huang

Despite the remarkable progress in person re-identification (Re-ID), such approaches still suffer from the failure cases where the discriminative body parts are missing. To mitigate this type of failure, we propose a simple yet effective Horizontal Pyramid Matching (HPM) approach to fully exploit various partial information of a given person, so that correct person candidates can be identified even if some key parts are missing. With HPM, we make the following contributions to produce more robust feature representations for the Re-ID task: 1) we learn to classify using partial feature representations at different horizontal pyramid scales, which successfully enhance the discriminative capabilities of various person parts; 2) we exploit average and max pooling strategies to account for person-specific discriminative information in a global-local manner. To validate the effectiveness of our proposed HPM method, extensive experiments are conducted on three popular datasets including Market-1501, DukeMTMC- ReID and CUHK03. Respectively, we achieve mAP scores of 83. 1%, 74. 5% and 59. 7% on these challenging benchmarks, which are the new state-of-the-arts.

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AAAI Conference 2019 Conference Paper

Weakly Supervised Scene Parsing with Point-Based Distance Metric Learning

Rui Qian
Yunchao Wei
Honghui Shi
Jiachen Li
Jiaying Liu
Thomas Huang

Semantic scene parsing is suffering from the fact that pixellevel annotations are hard to be collected. To tackle this issue, we propose a Point-based Distance Metric Learning (PDML) in this paper. PDML does not require dense annotated masks and only leverages several labeled points that are much easier to obtain to guide the training process. Concretely, we leverage semantic relationship among the annotated points by encouraging the feature representations of the intra- and intercategory points to keep consistent, i. e. points within the same category should have more similar feature representations compared to those from different categories. We formulate such a characteristic into a simple distance metric loss, which collaborates with the point-wise cross-entropy loss to optimize the deep neural networks. Furthermore, to fully exploit the limited annotations, distance metric learning is conducted across different training images instead of simply adopting an image-dependent manner. We conduct extensive experiments on two challenging scene parsing benchmarks of PASCAL- Context and ADE 20K to validate the effectiveness of our PDML, and competitive mIoU scores are achieved.

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AAAI Conference 2016 Conference Paper

Epitomic Image Super-Resolution

Yingzhen Yang
Zhangyang Wang
Zhaowen Wang
Shiyu Chang
Ding Liu
Honghui Shi
Thomas Huang

We propose Epitomic Image Super-Resolution (ESR) to enhance the current internal SR methods that exploit the selfsimilarities in the input. Instead of local nearest neighbor patch matching used in most existing internal SR methods, ESR employs epitomic patch matching that features robustness to noise, and both local and non-local patch matching. Extensive objective and subjective evaluation demonstrate the effectiveness and advantage of ESR on various images.

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