Search Results for author: Zenglin Shi

Found 11 papers, 6 papers with code

Reason from Context with Self-supervised Learning

no code implementations23 Nov 2022 Xiao Liu, Ankur Sikarwar, Gabriel Kreiman, Zenglin Shi, Mengmi Zhang

To better accommodate the object-centric nature of current downstream tasks such as object recognition and detection, various methods have been proposed to suppress contextual biases or disentangle objects from contexts.

Object Recognition Self-Supervised Learning +1

On the Robustness, Generalization, and Forgetting of Shape-Texture Debiased Continual Learning

no code implementations21 Nov 2022 Zenglin Shi, Ying Sun, Joo Hwee Lim, Mengmi Zhang

Tremendous progress has been made in continual learning to maintain good performance on old tasks when learning new tasks by tackling the catastrophic forgetting problem of neural networks.

Continual Learning

Frequency-Supervised MR-to-CT Image Synthesis

1 code implementation19 Jul 2021 Zenglin Shi, Pascal Mettes, Guoyan Zheng, Cees Snoek

In this paper, we find that all existing approaches share a common limitation: reconstruction breaks down in and around the high-frequency parts of CT images.

Computed Tomography (CT) Image Generation +1

On Measuring and Controlling the Spectral Bias of the Deep Image Prior

1 code implementation2 Jul 2021 Zenglin Shi, Pascal Mettes, Subhransu Maji, Cees G. M. Snoek

The deep image prior showed that a randomly initialized network with a suitable architecture can be trained to solve inverse imaging problems by simply optimizing it's parameters to reconstruct a single degraded image.

Denoising Super-Resolution

Unsharp Mask Guided Filtering

1 code implementation2 Jun 2021 Zenglin Shi, Yunlu Chen, Efstratios Gavves, Pascal Mettes, Cees G. M. Snoek

The state-of-the-art leverages deep networks to estimate the two core coefficients of the guided filter.


Ordered or Orderless: A Revisit for Video based Person Re-Identification

no code implementations24 Dec 2019 Le Zhang, Zenglin Shi, Joey Tianyi Zhou, Ming-Ming Cheng, Yun Liu, Jia-Wang Bian, Zeng Zeng, Chunhua Shen

Specifically, with a diagnostic analysis, we show that the recurrent structure may not be effective to learn temporal dependencies than what we expected and implicitly yields an orderless representation.

Video-Based Person Re-Identification

Robust Regression via Deep Negative Correlation Learning

no code implementations24 Aug 2019 Le Zhang, Zenglin Shi, Ming-Ming Cheng, Yun Liu, Jia-Wang Bian, Joey Tianyi Zhou, Guoyan Zheng, Zeng Zeng

Nonlinear regression has been extensively employed in many computer vision problems (e. g., crowd counting, age estimation, affective computing).

Age Estimation Crowd Counting +2

Counting with Focus for Free

1 code implementation ICCV 2019 Zenglin Shi, Pascal Mettes, Cees G. M. Snoek

To assist both the density estimation and the focus from segmentation, we also introduce an improved kernel size estimator for the point annotations.

Density Estimation

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