Search Results for author: Zudi Lin

Found 20 papers, 13 papers with code

Structure-Preserving Instance Segmentation via Skeleton-Aware Distance Transform

no code implementations8 Oct 2023 Zudi Lin, Donglai Wei, Aarush Gupta, Xingyu Liu, Deqing Sun, Hanspeter Pfister

Objects with complex structures pose significant challenges to existing instance segmentation methods that rely on boundary or affinity maps, which are vulnerable to small errors around contacting pixels that cause noticeable connectivity change.

Image Segmentation Instance Segmentation +3

Domain-Scalable Unpaired Image Translation via Latent Space Anchoring

1 code implementation26 Jun 2023 Siyu Huang, Jie An, Donglai Wei, Zudi Lin, Jiebo Luo, Hanspeter Pfister

However, given a UNIT model trained on certain domains, it is difficult for current methods to incorporate new domains because they often need to train the full model on both existing and new domains.

Image-to-Image Translation Translation

CDDFuse: Correlation-Driven Dual-Branch Feature Decomposition for Multi-Modality Image Fusion

2 code implementations CVPR 2023 Zixiang Zhao, Haowen Bai, Jiangshe Zhang, Yulun Zhang, Shuang Xu, Zudi Lin, Radu Timofte, Luc van Gool

We then introduce a dual-branch Transformer-CNN feature extractor with Lite Transformer (LT) blocks leveraging long-range attention to handle low-frequency global features and Invertible Neural Networks (INN) blocks focusing on extracting high-frequency local information.

object-detection Object Detection +1

MST++: Multi-stage Spectral-wise Transformer for Efficient Spectral Reconstruction

3 code implementations17 Apr 2022 Yuanhao Cai, Jing Lin, Zudi Lin, Haoqian Wang, Yulun Zhang, Hanspeter Pfister, Radu Timofte, Luc van Gool

Existing leading methods for spectral reconstruction (SR) focus on designing deeper or wider convolutional neural networks (CNNs) to learn the end-to-end mapping from the RGB image to its hyperspectral image (HSI).

Spectral Reconstruction Spectral Super-Resolution

Texture-Based Error Analysis for Image Super-Resolution

no code implementations CVPR 2022 Salma Abdel Magid, Zudi Lin, Donglai Wei, Yulun Zhang, Jinjin Gu, Hanspeter Pfister

Our key contribution is to leverage a texture classifier, which enables us to assign patches with semantic labels, to identify the source of SR errors both globally and locally.

Image Super-Resolution SSIM

PyTorch Connectomics: A Scalable and Flexible Segmentation Framework for EM Connectomics

1 code implementation10 Dec 2021 Zudi Lin, Donglai Wei, Jeff Lichtman, Hanspeter Pfister

We present PyTorch Connectomics (PyTC), an open-source deep-learning framework for the semantic and instance segmentation of volumetric microscopy images, built upon PyTorch.

Instance Segmentation Segmentation +1

Asymmetric 3D Context Fusion for Universal Lesion Detection

1 code implementation17 Sep 2021 Jiancheng Yang, Yi He, Kaiming Kuang, Zudi Lin, Hanspeter Pfister, Bingbing Ni

The proposed A3D consistently outperforms symmetric context fusion operators by considerable margins, and establishes a new \emph{state of the art} on DeepLesion.

Computed Tomography (CT) Lesion Detection +1

Discrete Cosine Transform Network for Guided Depth Map Super-Resolution

2 code implementations CVPR 2022 Zixiang Zhao, Jiangshe Zhang, Shuang Xu, Zudi Lin, Hanspeter Pfister

Guided depth super-resolution (GDSR) is an essential topic in multi-modal image processing, which reconstructs high-resolution (HR) depth maps from low-resolution ones collected with suboptimal conditions with the help of HR RGB images of the same scene.

Depth Map Super-Resolution

Dynamic High-Pass Filtering and Multi-Spectral Attention for Image Super-Resolution

no code implementations ICCV 2021 Salma Abdel Magid, Yulun Zhang, Donglai Wei, Won-Dong Jang, Zudi Lin, Yun Fu, Hanspeter Pfister

Specifically, we propose a dynamic high-pass filtering (HPF) module that locally applies adaptive filter weights for each spatial location and channel group to preserve high-frequency signals.

Image Super-Resolution

A Topological Nomenclature for 3D Shape Analysis in Connectomics

1 code implementation27 Sep 2019 Abhimanyu Talwar, Zudi Lin, Donglai Wei, Yuesong Wu, Bowen Zheng, Jinglin Zhao, Won-Dong Jang, Xueying Wang, Jeff W. Lichtman, Hanspeter Pfister

Next, we develop nomenclature rules for pyramidal neurons and mitochondria from the reduced graph and finally learn the feature embedding for shape manipulation.

3D Shape Classification 3D Shape Retrieval +1

White-Box Adversarial Defense via Self-Supervised Data Estimation

1 code implementation13 Sep 2019 Zudi Lin, Hanspeter Pfister, Ziming Zhang

In this paper, we study the problem of how to defend classifiers against adversarial attacks that fool the classifiers using subtly modified input data.

Adversarial Defense Self-Supervised Learning +1

FDive: Learning Relevance Models using Pattern-based Similarity Measures

no code implementations29 Jul 2019 Frederik L. Dennig, Tom Polk, Zudi Lin, Tobias Schreck, Hanspeter Pfister, Michael Behrisch

The detection of interesting patterns in large high-dimensional datasets is difficult because of their dimensionality and pattern complexity.

Active Learning feature selection

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