1k citations · 2.4k across the 22 of their papers we have counts for
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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…
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…
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…
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…
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…