Search Results for author: Xiao Jin

Found 12 papers, 5 papers with code

HODINet: High-Order Discrepant Interaction Network for RGB-D Salient Object Detection

no code implementations3 Jul 2023 Kang Yi, Jing Xu, Xiao Jin, Fu Guo, Yan-Feng Wu

Specifically, we design a high-order spatial fusion (HOSF) module and a high-order channel fusion (HOCF) module to fuse features of the first two and the last two stages, respectively.

object-detection RGB-D Salient Object Detection +1

Component-aware anomaly detection framework for adjustable and logical industrial visual inspection

1 code implementation15 May 2023 Tongkun Liu, Bing Li, Xiao Du, Bingke Jiang, Xiao Jin, Liuyi Jin, Zhuo Zhao

Meanwhile, segmenting a product image into multiple components provides a novel perspective for industrial visual inspection, demonstrating great potential in model customization, noise resistance, and anomaly classification.

Anomaly Classification Anomaly Detection +1

TriPINet: Tripartite Progressive Integration Network for Image Manipulation Localization

no code implementations25 Dec 2022 Wei-Yun Liang, Jing Xu, Xiao Jin

In this paper, we propose a tripartite progressive integration network (TriPINet) for end-to-end image manipulation localization.

Image Forensics Image Manipulation

CAFE: Catastrophic Data Leakage in Vertical Federated Learning

1 code implementation26 Oct 2021 Xiao Jin, Pin-Yu Chen, Chia-Yi Hsu, Chia-Mu Yu, Tianyi Chen

We name our proposed method as catastrophic data leakage in vertical federated learning (CAFE).

Federated Learning

CAFE: Catastrophic Data Leakage in Federated Learning

no code implementations1 Jan 2021 Xiao Jin, Ruijie Du, Pin-Yu Chen, Tianyi Chen

In this paper, we revisit this defense premise and propose an advanced data leakage attack to efficiently recover batch data from the shared aggregated gradients.

Federated Learning

VAFL: a Method of Vertical Asynchronous Federated Learning

no code implementations12 Jul 2020 Tianyi Chen, Xiao Jin, Yuejiao Sun, Wotao Yin

Horizontal Federated learning (FL) handles multi-client data that share the same set of features, and vertical FL trains a better predictor that combine all the features from different clients.

Federated Learning

Knowledge Distillation via Route Constrained Optimization

1 code implementation ICCV 2019 Xiao Jin, Baoyun Peng, Yi-Chao Wu, Yu Liu, Jiaheng Liu, Ding Liang, Xiaolin Hu

However, we find that the representation of a converged heavy model is still a strong constraint for training a small student model, which leads to a high lower bound of congruence loss.

Face Recognition Knowledge Distillation

Correlation Congruence for Knowledge Distillation

2 code implementations ICCV 2019 Baoyun Peng, Xiao Jin, Jiaheng Liu, Shunfeng Zhou, Yi-Chao Wu, Yu Liu, Dongsheng Li, Zhaoning Zhang

Most teacher-student frameworks based on knowledge distillation (KD) depend on a strong congruent constraint on instance level.

Face Recognition Image Classification +3

Transductive Centroid Projection for Semi-supervised Large-scale Recognition

no code implementations ECCV 2018 Yu Liu, Guanglu Song, Jing Shao, Xiao Jin, Xiaogang Wang

It is inspired by the observation of the weights in classification layer (called extit{anchors}) converge to the central direction of each class in hyperspace.

Clustering General Classification

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