Search Results for author: Junyu Luo

Found 11 papers, 2 papers with code

3D-SPS: Single-Stage 3D Visual Grounding via Referred Point Progressive Selection

1 code implementation CVPR 2022 Junyu Luo, Jiahui Fu, Xianghao Kong, Chen Gao, Haibing Ren, Hao Shen, Huaxia Xia, Si Liu

3D visual grounding aims to locate the referred target object in 3D point cloud scenes according to a free-form language description.

Visual Grounding

MedAttacker: Exploring Black-Box Adversarial Attacks on Risk Prediction Models in Healthcare

no code implementations11 Dec 2021 Muchao Ye, Junyu Luo, Guanjie Zheng, Cao Xiao, Ting Wang, Fenglong Ma

Deep neural networks (DNNs) have been broadly adopted in health risk prediction to provide healthcare diagnoses and treatments.

Adversarial Attack

FedSkel: Efficient Federated Learning on Heterogeneous Systems with Skeleton Gradients Update

1 code implementation20 Aug 2021 Junyu Luo, Jianlei Yang, Xucheng Ye, Xin Guo, Weisheng Zhao

Federated learning aims to protect users' privacy while performing data analysis from different participants.

Federated Learning

TransRefer3D: Entity-and-Relation Aware Transformer for Fine-Grained 3D Visual Grounding

no code implementations5 Aug 2021 Dailan He, Yusheng Zhao, Junyu Luo, Tianrui Hui, Shaofei Huang, Aixi Zhang, Si Liu

Existing works usually adopt dynamic graph networks to indirectly model the intra/inter-modal interactions, making the model difficult to distinguish the referred object from distractors due to the monolithic representations of visual and linguistic contents.

Visual Grounding

FedSiam: Towards Adaptive Federated Semi-Supervised Learning

no code implementations6 Dec 2020 Zewei Long, Liwei Che, Yaqing Wang, Muchao Ye, Junyu Luo, Jinze Wu, Houping Xiao, Fenglong Ma

In this paper, we focus on designing a general framework FedSiam to tackle different scenarios of federated semi-supervised learning, including four settings in the labels-at-client scenario and two setting in the labels-at-server scenario.

Federated Learning

A Benchmark Dataset for Understandable Medical Language Translation

no code implementations4 Dec 2020 Junyu Luo, Zifei Zheng, Hanzhong Ye, Muchao Ye, Yaqing Wang, Quanzeng You, Cao Xiao, Fenglong Ma

In this paper, we introduce MedLane -- a new human-annotated Medical Language translation dataset, to align professional medical sentences with layperson-understandable expressions.

Machine Translation Text Summarization +1

Accelerating CNN Training by Pruning Activation Gradients

no code implementations ECCV 2020 Xucheng Ye, Pengcheng Dai, Junyu Luo, Xin Guo, Yingjie Qi, Jianlei Yang, Yiran Chen

Sparsification is an efficient approach to accelerate CNN inference, but it is challenging to take advantage of sparsity in training procedure because the involved gradients are dynamically changed.

Learning Inverse Mapping by Autoencoder based Generative Adversarial Nets

no code implementations29 Mar 2017 Junyu Luo, Yong Xu, Chenwei Tang, Jiancheng Lv

The inverse mapping of GANs'(Generative Adversarial Nets) generator has a great potential value. Hence, some works have been developed to construct the inverse function of generator by directly learning or adversarial learning. While the results are encouraging, the problem is highly challenging and the existing ways of training inverse models of GANs have many disadvantages, such as hard to train or poor performance. Due to these reasons, we propose a new approach based on using inverse generator ($IG$) model as encoder and pre-trained generator ($G$) as decoder of an AutoEncoder network to train the $IG$ model.

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