Search Results for author: Ke Ma

Found 28 papers, 14 papers with code

Deep Learning Empowered Type-II Codebook: New Paradigm for Enhancing CSI Feedback

no code implementations14 May 2023 Ke Ma, Yiliang Sang, Yang Ming, Jin Lian, Chang Tian, Zhaocheng Wang

In contrast to its counterpart in Release 16, the Type-II codebook in Release 17 (R17) exploits the angular-delay-domain partial reciprocity between uplink and downlink channels and selects part of angular-delay-domain ports for measuring and feeding back the downlink CSI, where the performance of the conventional deep learning methods is limited due to the deficiency of sparse structures.

Vocal Bursts Type Prediction

An Ensemble Learning Approach for Exercise Detection in Type 1 Diabetes Patients

no code implementations11 May 2023 Ke Ma, Hongkai Chen, Shan Lin

Artificial pancreas (AP) systems have been developed as a solution for type 1 diabetic patients to mimic the behavior of the pancreas and regulate blood glucose levels.

Ensemble Learning

Improving Fast Adversarial Training with Prior-Guided Knowledge

no code implementations1 Apr 2023 Xiaojun Jia, Yong Zhang, Xingxing Wei, Baoyuan Wu, Ke Ma, Jue Wang, Xiaochun Cao

This initialization is generated by using high-quality adversarial perturbations from the historical training process.

Prompt-MIL: Boosting Multi-Instance Learning Schemes via Task-specific Prompt Tuning

no code implementations21 Mar 2023 Jingwei Zhang, Saarthak Kapse, Ke Ma, Prateek Prasanna, Joel Saltz, Maria Vakalopoulou, Dimitris Samaras

Compared to conventional full fine-tuning approaches, we fine-tune less than 1. 3% of the parameters, yet achieve a relative improvement of 1. 29%-13. 61% in accuracy and 3. 22%-27. 18% in AUROC and reduce GPU memory consumption by 38%-45% while training 21%-27% faster.

OMSN and FAROS: OCTA Microstructure Segmentation Network and Fully Annotated Retinal OCTA Segmentation Dataset

no code implementations26 Dec 2022 Peng Xiao, Xiaodong Hu, Ke Ma, Gengyuan Wang, Ziqing Feng, Yuancong Huang, Jin Yuan

The lack of efficient segmentation methods and fully-labeled datasets limits the comprehensive assessment of optical coherence tomography angiography (OCTA) microstructures like retinal vessel network (RVN) and foveal avascular zone (FAZ), which are of great value in ophthalmic and systematic diseases evaluation.

Precise Location Matching Improves Dense Contrastive Learning in Digital Pathology

1 code implementation23 Dec 2022 Jingwei Zhang, Saarthak Kapse, Ke Ma, Prateek Prasanna, Maria Vakalopoulou, Joel Saltz, Dimitris Samaras

Our method outperforms previous dense matching methods by up to 7. 2% in average precision for detection and 5. 6% in average precision for instance segmentation tasks.

Contrastive Learning Instance Segmentation +1

A Tale of HodgeRank and Spectral Method: Target Attack Against Rank Aggregation Is the Fixed Point of Adversarial Game

1 code implementation13 Sep 2022 Ke Ma, Qianqian Xu, Jinshan Zeng, Guorong Li, Xiaochun Cao, Qingming Huang

From the perspective of the dynamical system, the attack behavior with a target ranking list is a fixed point belonging to the composition of the adversary and the victim.

Information Retrieval Retrieval

Prior-Guided Adversarial Initialization for Fast Adversarial Training

1 code implementation18 Jul 2022 Xiaojun Jia, Yong Zhang, Xingxing Wei, Baoyuan Wu, Ke Ma, Jue Wang, Xiaochun Cao

Based on the observation, we propose a prior-guided FGSM initialization method to avoid overfitting after investigating several initialization strategies, improving the quality of the AEs during the whole training process.

Gigapixel Whole-Slide Images Classification using Locally Supervised Learning

1 code implementation17 Jul 2022 Jingwei Zhang, Xin Zhang, Ke Ma, Rajarsi Gupta, Joel Saltz, Maria Vakalopoulou, Dimitris Samaras

Histopathology whole slide images (WSIs) play a very important role in clinical studies and serve as the gold standard for many cancer diagnoses.

Classification Multiple Instance Learning +1

LAS-AT: Adversarial Training with Learnable Attack Strategy

1 code implementation CVPR 2022 Xiaojun Jia, Yong Zhang, Baoyuan Wu, Ke Ma, Jue Wang, Xiaochun Cao

In this paper, we propose a novel framework for adversarial training by introducing the concept of "learnable attack strategy", dubbed LAS-AT, which learns to automatically produce attack strategies to improve the model robustness.

Visual attention analysis of pathologists examining whole slide images of Prostate cancer

no code implementations17 Feb 2022 Souradeep Chakraborty, Ke Ma, Rajarsi Gupta, Beatrice Knudsen, Gregory J. Zelinsky, Joel H. Saltz, Dimitris Samaras

To quantify the relationship between a pathologist's attention and evidence for cancer in the WSI, we obtained tumor annotations from a genitourinary specialist.

Navigate whole slide images

Learning Surface Parameterization for Document Image Unwarping

no code implementations29 Sep 2021 Sagnik Das, Ke Ma, Zhixin Shu, Dimitris Samaras

We also demonstrate the usefulness of our system by applying it to document texture editing.

3D Scene Reconstruction

Efficient Semi-Discrete Optimal Transport Using the Maximum Relative Error between Distributions

no code implementations29 Sep 2021 Huidong Liu, Ke Ma, Lei Zhou, Dimitris Samaras

If the \texttt{MRE} is smaller than 1, then every target point is guaranteed to have an area in the source distribution that is mapped to it.

Poisoning Attack against Estimating from Pairwise Comparisons

1 code implementation5 Jul 2021 Ke Ma, Qianqian Xu, Jinshan Zeng, Xiaochun Cao, Qingming Huang

In this paper, to the best of our knowledge, we initiate the first systematic investigation of data poisoning attacks on pairwise ranking algorithms, which can be formalized as the dynamic and static games between the ranker and the attacker and can be modeled as certain kinds of integer programming problems.

Data Poisoning

HAIR: Head-mounted AR Intention Recognition

no code implementations22 Feb 2021 David Puljiz, BoWen Zhou, Ke Ma, Björn Hein

In this paper we propose an intention recognition system that is based purely on a portable head-mounted display.

Intent Detection Robotics Human-Computer Interaction

AdaSpring: Context-adaptive and Runtime-evolutionary Deep Model Compression for Mobile Applications

no code implementations28 Jan 2021 Sicong Liu, Bin Guo, Ke Ma, Zhiwen Yu, Junzhao Du

There are many deep learning (e. g., DNN) powered mobile and wearable applications today continuously and unobtrusively sensing the ambient surroundings to enhance all aspects of human lives.

Model Compression

Deep Learning Assisted Calibrated Beam Training for Millimeter-Wave Communication Systems

1 code implementation8 Jan 2021 Ke Ma, Dongxuan He, Hancun Sun, Zhaocheng Wang, Sheng Chen

To tackle this problem, the second scheme adopts long-short term memory (LSTM) network for tracking the movement of users and calibrating the beam direction according to the received signals of prior beam training, in order to enhance the robustness to noise.

On Stochastic Variance Reduced Gradient Method for Semidefinite Optimization

no code implementations1 Jan 2021 Jinshan Zeng, Yixuan Zha, Ke Ma, Yuan YAO

In this paper, we fill this gap via exploiting a new semi-stochastic variant of the original SVRG with Option I adapted to the semidefinite optimization.

Intrinsic Decomposition of Document Images In-the-Wild

1 code implementation29 Nov 2020 Sagnik Das, Hassan Ahmed Sial, Ke Ma, Ramon Baldrich, Maria Vanrell, Dimitris Samaras

However, document shadow or shading removal results still suffer because: (a) prior methods rely on uniformity of local color statistics, which limit their application on real-scenarios with complex document shapes and textures and; (b) synthetic or hybrid datasets with non-realistic, simulated lighting conditions are used to train the models.

Intrinsic Image Decomposition Optical Character Recognition (OCR) +1

Deep Learning Assisted mmWave Beam Prediction with Prior Low-frequency Information

no code implementations30 Oct 2020 Ke Ma, Dongxuan He, Hancun Sun, Zhaocheng Wang

Huge overhead of beam training poses a significant challenge to mmWave communications.

Attribute-guided image generation from layout

2 code implementations27 Aug 2020 Ke Ma, Bo Zhao, Leonid Sigal

Also, the generated images from our model have higher resolution, object classification accuracy and consistency, as compared to the previous state-of-the-art.

Image Generation Semantic Segmentation

Fast Stochastic Ordinal Embedding with Variance Reduction and Adaptive Step Size

no code implementations1 Dec 2019 Ke Ma, Jinshan Zeng, Qianqian Xu, Xiaochun Cao, Wei Liu, Yuan YAO

Learning representation from relative similarity comparisons, often called ordinal embedding, gains rising attention in recent years.

Robust Ordinal Embedding from Contaminated Relative Comparisons

1 code implementation5 Dec 2018 Ke Ma, Qianqian Xu, Xiaochun Cao

Existing ordinal embedding methods usually follow a two-stage routine: outlier detection is first employed to pick out the inconsistent comparisons; then an embedding is learned from the clean data.

Outlier Detection

Less but Better: Generalization Enhancement of Ordinal Embedding via Distributional Margin

1 code implementation5 Dec 2018 Ke Ma, Qianqian Xu, Zhiyong Yang, Xiaochun Cao

To address the issue of insufficient training samples, we propose a margin distribution learning paradigm for ordinal embedding, entitled Distributional Margin based Ordinal Embedding (\textit{DMOE}).

DocUNet: Document Image Unwarping via a Stacked U-Net

1 code implementation CVPR 2018 Ke Ma, Zhixin Shu, Xue Bai, Jue Wang, Dimitris Samaras

The network is trained on this dataset with various data augmentations to improve its generalization ability.

Ranked #4 on SSIM on DocUNet (using extra training data)

Local Distortion MS-SSIM +1

Stochastic Non-convex Ordinal Embedding with Stabilized Barzilai-Borwein Step Size

1 code implementation17 Nov 2017 Ke Ma, Jinshan Zeng, Jiechao Xiong, Qianqian Xu, Xiaochun Cao, Wei Liu, Yuan YAO

Learning representation from relative similarity comparisons, often called ordinal embedding, gains rising attention in recent years.

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