427 citations · 1.1k across the 82 of their papers we have counts for
14 papers · 1 filter
Deformable Gabor Feature Networks for Biomedical Image Classification
Xuan Gong, Xin Xia, Wentao Zhu +3
In recent years, deep learning has dominated progress in the field of medical image analysis. We find however, that the ability of current deep learning approaches to represent the…
Fast Class-wise Updating for Online Hashing
Mingbao Lin, Rongrong Ji, Xiaoshuai Sun +4
Online image hashing has received increasing research attention recently, which processes large-scale data in a streaming fashion to update the hash functions on-the-fly. To this e…
A Review of Recent Advances of Binary Neural Networks for Edge Computing
Wenyu Zhao, Teli Ma, Xuan Gong +2
Edge computing is promising to become one of the next hottest topics in artificial intelligence because it benefits various evolving domains such as real-time unmanned aerial syste…
PAMS: Quantized Super-Resolution via Parameterized Max Scale
Huixia Li, Chenqian Yan, Shaohui Lin +5
Deep convolutional neural networks (DCNNs) have shown dominant performance in the task of super-resolution (SR). However, their heavy memory cost and computation overhead significa…
Rotated Binary Neural Network
Mingbao Lin, Rongrong Ji, Zihan Xu +5
Binary Neural Network (BNN) shows its predominance in reducing the complexity of deep neural networks. However, it suffers severe performance degradation. One of the major impedime…
The 1st Tiny Object Detection Challenge:Methods and Results
Xuehui Yu, Zhenjun Han, Yuqi Gong +22
The 1st Tiny Object Detection (TOD) Challenge aims to encourage research in developing novel and accurate methods for tiny object detection in images which have wide views, with a…
Binarized Neural Architecture Search for Efficient Object Recognition
Hanlin Chen, Li'an Zhuo, Baochang Zhang +5
Traditional neural architecture search (NAS) has a significant impact in computer vision by automatically designing network architectures for various tasks. In this paper, binarize…
Anti-Bandit Neural Architecture Search for Model Defense
Hanlin Chen, Baochang Zhang, Song Xue +4
Deep convolutional neural networks (DCNNs) have dominated as the best performers in machine learning, but can be challenged by adversarial attacks. In this paper, we defend against…
iffDetector: Inference-aware Feature Filtering for Object Detection
Mingyuan Mao, Yuxin Tian, Baochang Zhang +4
Modern CNN-based object detectors focus on feature configuration during training but often ignore feature optimization during inference. In this paper, we propose a new feature opt…
Cogradient Descent for Bilinear Optimization
Li'an Zhuo, Baochang Zhang, Linlin Yang +5
Conventional learning methods simplify the bilinear model by regarding two intrinsically coupled factors independently, which degrades the optimization procedure. One reason lies i…
CP-NAS: Child-Parent Neural Architecture Search for Binary Neural Networks
Li'an Zhuo, Baochang Zhang, Hanlin Chen +4
Neural architecture search (NAS) proves to be among the best approaches for many tasks by generating an application-adaptive neural architecture, which is still challenged by high…
HRank: Filter Pruning using High-Rank Feature Map
Mingbao Lin, Rongrong Ji, Yan Wang +4
Neural network pruning offers a promising prospect to facilitate deploying deep neural networks on resource-limited devices. However, existing methods are still challenged by the t…
NAS-Count: Counting-by-Density with Neural Architecture Search
Yutao Hu, Xiaolong Jiang, Xuhui Liu +4
Most of the recent advances in crowd counting have evolved from hand-designed density estimation networks, where multi-scale features are leveraged to address the scale variation p…
Channel Pruning via Automatic Structure Search
Mingbao Lin, Rongrong Ji, Yuxin Zhang +3
Channel pruning is among the predominant approaches to compress deep neural networks. To this end, most existing pruning methods focus on selecting channels (filters) by importance…