1k citations · 2.4k across the 22 of their papers we have counts for
22 papers
Dynamic Coarse-to-Fine Learning for Oriented Tiny Object Detection
Chang Xu, Jian Ding, Jinwang Wang +4
Detecting arbitrarily oriented tiny objects poses intense challenges to existing detectors, especially for label assignment. Despite the exploration of adaptive label assignment in…
RFLA: Gaussian Receptive Field based Label Assignment for Tiny Object Detection
Chang Xu, Jinwang Wang, Wen Yang +3
Detecting tiny objects is one of the main obstacles hindering the development of object detection. The performance of generic object detectors tends to drastically deteriorate on t…
Detecting tiny objects in aerial images: A normalized Wasserstein distance and a new benchmark
Chang Xu, Jinwang Wang, Wen Yang +3
Tiny object detection (TOD) in aerial images is challenging since a tiny object only contains a few pixels. State-of-the-art object detectors do not provide satisfactory results on…
An Empirical Study of Adder Neural Networks for Object Detection
Xinghao Chen, Chang Xu, Minjing Dong +2
Adder neural networks (AdderNets) have shown impressive performance on image classification with only addition operations, which are more energy efficient than traditional convolut…
A Normalized Gaussian Wasserstein Distance for Tiny Object Detection
Jinwang Wang, Chang Xu, Wen Yang +1
Detecting tiny objects is a very challenging problem since a tiny object only contains a few pixels in size. We demonstrate that state-of-the-art detectors do not produce satisfact…
Optical Flow Distillation: Towards Efficient and Stable Video Style Transfer
Xinghao Chen, Yiman Zhang, Yunhe Wang +3
Video style transfer techniques inspire many exciting applications on mobile devices. However, their efficiency and stability are still far from satisfactory. To boost the transfer…
ReNAS:Relativistic Evaluation of Neural Architecture Search
Yixing Xu, Yunhe Wang, Kai Han +4
An effective and efficient architecture performance evaluation scheme is essential for the success of Neural Architecture Search (NAS). To save computational cost, most of existing…
Full-Stack Filters to Build Minimum Viable CNNs
Kai Han, Yunhe Wang, Yixing Xu +3
Deep convolutional neural networks (CNNs) are usually over-parameterized, which cannot be easily deployed on edge devices such as mobile phones and smart cameras. Existing works us…
Bringing Giant Neural Networks Down to Earth with Unlabeled Data
Yehui Tang, Shan You, Chang Xu +2
Compressing giant neural networks has gained much attention for their extensive applications on edge devices such as cellphones. During the compressing process, one of the most imp…
Multi-view Vector-valued Manifold Regularization for Multi-label Image Classification
Yong Luo, Dacheng Tao, Chang Xu +3
In computer vision, image datasets used for classification are naturally associated with multiple labels and comprised of multiple views, because each image may contain several obj…
Multi-View Intact Space Learning
Chang Xu, Dacheng Tao, Chao Xu
It is practical to assume that an individual view is unlikely to be sufficient for effective multi-view learning. Therefore, integration of multi-view information is both valuable…
Cost-Sensitive Feature Selection by Optimizing F-Measures
Meng Liu, Chang Xu, Yong Luo +3
Feature selection is beneficial for improving the performance of general machine learning tasks by extracting an informative subset from the high-dimensional features. Conventional…
Attention-GAN for Object Transfiguration in Wild Images
Xinyuan Chen, Chang Xu, Xiaokang Yang +1
This paper studies the object transfiguration problem in wild images. The generative network in classical GANs for object transfiguration often undertakes a dual responsibility: to…
Evolutionary Generative Adversarial Networks
Chaoyue Wang, Chang Xu, Xin Yao +1
Generative adversarial networks (GAN) have been effective for learning generative models for real-world data. However, existing GANs (GAN and its variants) tend to suffer from trai…
Towards Evolutional Compression
Yunhe Wang, Chang Xu, Jiayan Qiu +2
Compressing convolutional neural networks (CNNs) is essential for transferring the success of CNNs to a wide variety of applications to mobile devices. In contrast to directly reco…
Perceptual Adversarial Networks for Image-to-Image Transformation
Chaoyue Wang, Chang Xu, Chaohui Wang +1
In this paper, we propose a principled Perceptual Adversarial Networks (PAN) for image-to-image transformation tasks. Unlike existing application-specific algorithms, PAN provides…
Privileged Multi-label Learning
Shan You, Chang Xu, Yunhe Wang +2
This paper presents privileged multi-label learning (PrML) to explore and exploit the relationship between labels in multi-label learning problems. We suggest that for each individ…
Streaming View Learning
Chang Xu, Dacheng Tao, Chao Xu
An underlying assumption in conventional multi-view learning algorithms is that all views can be simultaneously accessed. However, due to various factors when collecting and pre-pr…
Parts for the Whole: The DCT Norm for Extreme Visual Recovery
Yunhe Wang, Chang Xu, Shan You +2
Here we study the extreme visual recovery problem, in which over 90\% of pixel values in a given image are missing. Existing low rank-based algorithms are only effective for recove…
Streaming Label Learning for Modeling Labels on the Fly
Shan You, Chang Xu, Yunhe Wang +2
It is challenging to handle a large volume of labels in multi-label learning. However, existing approaches explicitly or implicitly assume that all the labels in the learning proce…
Local Rademacher Complexity for Multi-label Learning
Chang Xu, Tongliang Liu, Dacheng Tao +1
We analyze the local Rademacher complexity of empirical risk minimization (ERM)-based multi-label learning algorithms, and in doing so propose a new algorithm for multi-label learn…
A Survey on Multi-view Learning
Chang Xu, Dacheng Tao, Chao Xu
In recent years, a great many methods of learning from multi-view data by considering the diversity of different views have been proposed. These views may be obtained from multiple…