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	1. Adaptive Margin of Triplet-Center Loss for Deep Metric Learning
	YAO Li,ZHANG Bin
	Computer Science and Technology 06 January 2021
	Show/Hide Abstract \| Cite this paper︱Full-text: PDF (901K B)

	Abstract:In the family loss functions built on pair-based, most of them need to manually tune uniform thresholds between pairs to optimize the parameters of network. However, those hyper-parameters are fixed which is unreasonable for the reason that any two classes have different similarity. What’s more, it has to cost too much time and energy to tune the hyper-parameters for each task to find suitable values. Therefore, this paper proposes a novel loss named adaptive margin of triplet-center loss (AMTCL), which can learn a specific margin for a center of each class, while keep inter-class separateness, enhance the discriminative power of features and lighten our burden. Finally, the proposed AMTCL obtains state-of-the-art performance on four image retrieval benchmarks. Without whistle and blow, the proposed loss only need a few codes can be easily implemented in current network.
	TO cite this article:YAO Li,ZHANG Bin. Adaptive Margin of Triplet-Center Loss for Deep Metric Learning[OL].[ 6 January 2021] http://en.paper.edu.cn/en_releasepaper/content/4753303


	2. Medical Image Segmentation based on Octave Convolution
	Zhang Qiong,Tan Guanghua
	Computer Science and Technology 10 June 2020
	Show/Hide Abstract \| Cite this paper︱Full-text: PDF (757K B)

	Abstract:Medical image segmentation is a very vital research field in computer vision. How to realize an instant and accurate segmentation is of great importance in medical image segmentation. Image segmentation based on deep learning technique can be described as an encoder-decoder architecture. The most classic existed encoder-decoder model is U-Net. However, it can not solve the blurred boundary problem in predicting the segmentation result of the high resolution image. Therefore, this paper proposes a deep learning method that is based on boundary information. This paper proposes adopting Octave convolution to decompose the features into low-frequency feature and high-frequency feature and utilizing the low spatial frequency component to get the segmentation of the smoothly changing structure in the original image and the high spatial frequency component to get the segmentation of the rapidly changing fine details in the original image, followed by using the segmentation of fine details as the constrain condition. This paper proposes concatenating the smoothly changing structure segmentation and the rapidly changing fine details segmentation to realize the constrain condition. The segmentation result of the whole original image is obtained by putting the concatenated segmentation into the convolutional layer for class prediction. Meanwhile, this paper considers the class imbalance problem in the multi-class segmentation and proposes giving more weight to the rare classes. Because this paper adopts Octave convolution and the encoder-decoder method as U-Net, this paper calls the proposed approach Oct-UNet. This proposed method can not only achieve better results than U-Net, but also contains less parameters. The following conducted experiments verify the effect of the proposed approach.
	TO cite this article:Zhang Qiong,Tan Guanghua. Medical Image Segmentation based on Octave Convolution[OL].[10 June 2020] http://en.paper.edu.cn/en_releasepaper/content/4752349


	3. DCDN: Double Cross & Deep Network for News Recommandation
	Zhihong Yang,Yulong Wang
	Computer Science and Technology 18 March 2020
	Show/Hide Abstract \| Cite this paper︱Full-text: PDF (739K B)

	Abstract:The recommendation system is widely used in Internet products, and the recommendation algorithm is paid more and more attention by researchers. This paper proposes Double Cross & Deep Network (DCDN) algorithm for news recommendation. On the basis of DCN network, this algorithm proposes a new double-crossing depth network, which extracts the features of "related news" in the recommended candidate set separately, and displays the feature crossing with the user information and the seed news information respectively. The two Cross networks and Deep networks of DCDN are independent from each other. Cross Netword is used to obtain the Cross information between features, and Deep Network is used to model high-order nonlinear features. Users can change Network parameters according to the prediction requirements. Among them, SR-Cross is used to ensure the correlation between seed news and recommended news, and UR-Cross combines with user portrait to improve users\' reading interest. The experiment on two real data sets proves that the DCDN algorithm has better accuracy performance compared with other deep learning models and is practical in engineering while guaranteeing the speed.
	TO cite this article:Zhihong Yang,Yulong Wang. DCDN: Double Cross & Deep Network for News Recommandation[OL].[18 March 2020] http://en.paper.edu.cn/en_releasepaper/content/4751250


	4. Multimodal Information Fusion Based Housing Prices Prediction
	CHANG Cheng,ZHANG Zhongbao
	Computer Science and Technology 27 May 2019
	Show/Hide Abstract \| Cite this paper︱Full-text: PDF (422K B)

	Abstract:Housing price prediction has caught much attention and has been researched for a long time, and it is known to all that the value of a house is influenced by a wealth of determinants, some of which are irregular or even cannot be quantified. Moreover, housing prices fluctuate tremendously in reality. In this scenario, it remains a challenging task: To design an accurate, multi-dimensional predictive method of estimating housing prices. Previous work on this problem focuses on the value of housing independently and makes use of the structured features (such as floors, the number of rooms, etc.) to offer a valuation of the house. However, this assumption does not hold in reality since housing

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