Search Results for author: Jingtao Li

Found 12 papers, 8 papers with code

A Unified Remote Sensing Anomaly Detector Across Modalities and Scenes via Deviation Relationship Learning

1 code implementation11 Oct 2023 Jingtao Li, Xinyu Wang, Hengwei Zhao, Liangpei Zhang, Yanfei Zhong

Firstly, we reformulate the anomaly detection task as an undirected bilayer graph based on the deviation relationship, where the anomaly score is modeled as the conditional probability, given the pattern of the background and normal objects.

Anomaly Detection

Class Prior-Free Positive-Unlabeled Learning with Taylor Variational Loss for Hyperspectral Remote Sensing Imagery

1 code implementation ICCV 2023 Hengwei Zhao, Xinyu Wang, Jingtao Li, Yanfei Zhong

Positive-unlabeled learning (PU learning) in hyperspectral remote sensing imagery (HSI) is aimed at learning a binary classifier from positive and unlabeled data, which has broad prospects in various earth vision applications.

One-Step Detection Paradigm for Hyperspectral Anomaly Detection via Spectral Deviation Relationship Learning

1 code implementation22 Mar 2023 Jingtao Li, Xinyu Wang, Shaoyu Wang, Hengwei Zhao, Liangpei Zhang, Yanfei Zhong

In this paper, an unsupervised transferred direct detection (TDD) model is proposed, which is optimized directly for the anomaly detection task (one-step paradigm) and has transferability.

Anomaly Detection

Anomaly Segmentation for High-Resolution Remote Sensing Images Based on Pixel Descriptors

1 code implementation31 Jan 2023 Jingtao Li, Xinyu Wang, Hengwei Zhao, Shaoyu Wang, Yanfei Zhong

Anomaly segmentation in high spatial resolution (HSR) remote sensing imagery is aimed at segmenting anomaly patterns of the earth deviating from normal patterns, which plays an important role in various Earth vision applications.

One-Class Classification Segmentation

Split Federated Learning on Micro-controllers: A Keyword Spotting Showcase

no code implementations4 Oct 2022 Jingtao Li, Runcong Kuang

To prevent this, Federated Learning is proposed as a private learning scheme, using which users can locally train the model without collecting users' raw data to servers.

Federated Learning Keyword Spotting

An Adjustable Farthest Point Sampling Method for Approximately-sorted Point Cloud Data

1 code implementation18 Aug 2022 Jingtao Li, Jian Zhou, Yan Xiong, Xing Chen, Chaitali Chakrabarti

Sampling is an essential part of raw point cloud data processing such as in the popular PointNet++ scheme.

ResSFL: A Resistance Transfer Framework for Defending Model Inversion Attack in Split Federated Learning

1 code implementation CVPR 2022 Jingtao Li, Adnan Siraj Rakin, Xing Chen, Zhezhi He, Deliang Fan, Chaitali Chakrabarti

While such a scheme helps reduce the computational load at the client end, it opens itself to reconstruction of raw data from intermediate activation by the server.

Federated Learning

Communication and Computation Reduction for Split Learning using Asynchronous Training

no code implementations20 Jul 2021 Xing Chen, Jingtao Li, Chaitali Chakrabarti

An added benefit of the proposed communication reduction method is that the computations at the client side are reduced due to reduction in the number of client model updates.

Privacy Preserving

RADAR: Run-time Adversarial Weight Attack Detection and Accuracy Recovery

1 code implementation20 Jan 2021 Jingtao Li, Adnan Siraj Rakin, Zhezhi He, Deliang Fan, Chaitali Chakrabarti

In this work, we propose RADAR, a Run-time adversarial weight Attack Detection and Accuracy Recovery scheme to protect DNN weights against PBFA.

T-BFA: Targeted Bit-Flip Adversarial Weight Attack

2 code implementations24 Jul 2020 Adnan Siraj Rakin, Zhezhi He, Jingtao Li, Fan Yao, Chaitali Chakrabarti, Deliang Fan

Prior works of BFA focus on un-targeted attack that can hack all inputs into a random output class by flipping a very small number of weight bits stored in computer memory.

Adversarial Attack Image Classification

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