Proceedings ArticleDOI
Locating Waterfowl Farms from Satellite Images with Parallel Residual U-Net Architecture
Keng-Chih Chang,Tsung-Jung Liu,Kuan-Hsien Liu,Day-Yu Chao +3 more
- pp 114-119
TLDR
This work proposed a new method trying to directly locate waterfowl farms, including both registered and unregistered ones without the need of human labeling, and shows that using the existing simple U-Net combined with residual blocks has better performance than the other deep models in this task.Abstract:
For the epidemic prevention of avian influenza, there exist lots of differences between ideality and reality. This is why the epidemic is usually out of control. One of the reasons is that many illegal waterfowl farms are built without government registration. In this work, we proposed a new method trying to directly locate waterfowl farms, including both registered and unregistered ones without the need of human labeling. This will not only save human labors, but also update the location and size information of waterfowl farms regularly due to the computing speed of computers. In this work, we proposed a new method for satellite image augmentation. The layers of the model we proposed are not deeper than the other deep neural network models. However, we show that using the existing simple U-Net combined with residual blocks has better performance than the other deep models in this task.read more
Citations
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Proceedings ArticleDOI
SUNet: Swin Transformer UNet for Image Denoising
TL;DR: A restoration model called SUNet is proposed which uses the Swin Transformer layer as the authors' basic block and then is applied to UNet architecture for image denoising.
Proceedings ArticleDOI
SUNet: Swin Transformer UNet for Image Denoising
TL;DR: SUNet as mentioned in this paper uses the Swin Transformer layer as the basic block and then applies it to UNet architecture for image denoising, achieving state-of-the-art performance.
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Semantic Part Segmentation using Compositional Model combining Shape and Appearance
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Half Wavelet Attention on M-Net+ for Low-Light Image Enhancement
TL;DR: Fan et al. as discussed by the authors proposed an image enhancement network (HWMNet) based on an improved hierarchical model: M-Net+, which used a half wavelet attention block on M-net+ to enrich the features from wavelet domain.
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Improved Small Object Detection for Road Driving based on YOLO-R
TL;DR: This work proposes an algorithm based on YOLO-R to improve the detection accuracy to deal with the actual situation in this field, and experiments show that this method has better results than other models in this dataset.
References
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Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Proceedings ArticleDOI
Densely Connected Convolutional Networks
TL;DR: DenseNet as mentioned in this paper proposes to connect each layer to every other layer in a feed-forward fashion, which can alleviate the vanishing gradient problem, strengthen feature propagation, encourage feature reuse, and substantially reduce the number of parameters.
Book
Pattern Recognition and Machine Learning
TL;DR: Probability Distributions, linear models for Regression, Linear Models for Classification, Neural Networks, Graphical Models, Mixture Models and EM, Sampling Methods, Continuous Latent Variables, Sequential Data are studied.
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