211 citations · 688 across the 13 of their papers we have counts for
13 papers
Recognizing Vector Graphics without Rasterization
Xinyang Jiang, Lu Liu, Caihua Shan +3
In this paper, we consider a different data format for images: vector graphics. In contrast to raster graphics which are widely used in image recognition, vector graphics can be sc…
Full-Cycle Energy Consumption Benchmark for Low-Carbon Computer Vision
Bo Li, Xinyang Jiang, Donglin Bai +6
The energy consumption of deep learning models is increasing at a breathtaking rate, which raises concerns due to potential negative effects on carbon neutrality in the context of…
PyGlove: Symbolic Programming for Automated Machine Learning
Daiyi Peng, Xuanyi Dong, Esteban Real +7
Neural networks are sensitive to hyper-parameter and architecture choices. Automated Machine Learning (AutoML) is a promising paradigm for automating these choices. Current ML soft…
One-Shot Neural Architecture Search via Self-Evaluated Template Network
Xuanyi Dong, Yi Yang
Neural architecture search (NAS) aims to automate the search procedure of architecture instead of manual design. Even if recent NAS approaches finish the search within days, length…
Teacher Supervises Students How to Learn From Partially Labeled Images for Facial Landmark Detection
Xuanyi Dong, Yi Yang
Facial landmark detection aims to localize the anatomically defined points of human faces. In this paper, we study facial landmark detection from partially labeled facial images. A…
Network Pruning via Transformable Architecture Search
Xuanyi Dong, Yi Yang
Network pruning reduces the computation costs of an over-parameterized network without performance damage. Prevailing pruning algorithms pre-define the width and depth of the prune…
Auto-ReID: Searching for a Part-aware ConvNet for Person Re-Identification
Ruijie Quan, Xuanyi Dong, Yu Wu +2
Prevailing deep convolutional neural networks (CNNs) for person re-IDentification (reID) are usually built upon ResNet or VGG backbones, which were originally designed for classifi…
Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks
Yang He, Guoliang Kang, Xuanyi Dong +2
This paper proposed a Soft Filter Pruning (SFP) method to accelerate the inference procedure of deep Convolutional Neural Networks (CNNs). Specifically, the proposed SFP enables th…
Supervision-by-Registration: An Unsupervised Approach to Improve the Precision of Facial Landmark Detectors
Xuanyi Dong, Shoou-I Yu, Xinshuo Weng +3
In this paper, we present supervision-by-registration, an unsupervised approach to improve the precision of facial landmark detectors on both images and video. Our key observation…
Style Aggregated Network for Facial Landmark Detection
Xuanyi Dong, Yan Yan, Wanli Ouyang +1
Recent advances in facial landmark detection achieve success by learning discriminative features from rich deformation of face shapes and poses. Besides the variance of faces thems…
EraseReLU: A Simple Way to Ease the Training of Deep Convolution Neural Networks
Xuanyi Dong, Guoliang Kang, Kun Zhan +1
For most state-of-the-art architectures, Rectified Linear Unit (ReLU) becomes a standard component accompanied with each layer. Although ReLU can ease the network training to an ex…
PatchShuffle Regularization
Guoliang Kang, Xuanyi Dong, Liang Zheng +1
This paper focuses on regularizing the training of the convolutional neural network (CNN). We propose a new regularization approach named ``PatchShuffle`` that can be adopted in an…
More is Less: A More Complicated Network with Less Inference Complexity
Xuanyi Dong, Junshi Huang, Yi Yang +1
In this paper, we present a novel and general network structure towards accelerating the inference process of convolutional neural networks, which is more complicated in network st…