Models, code, and papers for "Xiaoyan Sun":

Gastric histopathology image segmentation using a hierarchical conditional random field

Mar 04, 2020
Changhao Sun, Chen Li, Xiaoyan Li

In this paper, a Hierarchical Conditional Random Field (HCRF) model based Gastric Histopathology Image Segmentation (GHIS) method is proposed, which can localize abnormal (cancer) regions in gastric histopathology images obtained by optical microscope to assist histopathologists in medical work. First, to obtain pixel-level segmentation information, we retrain a Convolutional Neural Network (CNN) to build up our pixel-level potentials. Then, in order to obtain abundant spatial segmentation information in patch-level, we fine-tune another three CNNs to build up our patch-level potentials. Thirdly, based on the pixel- and patch-level potentials, our HCRF model is structured. Finally, graph-based post-processing is applied to further improve our segmentation performance. In the experiment, a segmentation accuracy of 78.91% is achieved on a Hematoxylin and Eosin (H&E) stained gastric histopathological dataset with 560 images, showing the effectiveness and future potential of the proposed GHIS method.

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Communication-Efficient Federated Deep Learning with Asynchronous Model Update and Temporally Weighted Aggregation

Mar 18, 2019
Yang Chen, Xiaoyan Sun, Yaochu Jin

Federated learning obtains a central model on the server by aggregating models trained locally on clients. As a result, federated learning does not require clients to upload their data to the server, thereby preserving the data privacy of the clients. One challenge in federated learning is to reduce the client-server communication since the end devices typically have very limited communication bandwidth. This paper presents an enhanced federated learning technique by proposing a synchronous learning strategy on the clients and a temporally weighted aggregation of the local models on the server. In the asynchronous learning strategy, different layers of the deep neural networks are categorized into shallow and deeps layers and the parameters of the deep layers are updated less frequently than those of the shallow layers. Furthermore, a temporally weighted aggregation strategy is introduced on the server to make use of the previously trained local models, thereby enhancing the accuracy and convergence of the central model. The proposed algorithm is empirically on two datasets with different deep neural networks. Our results demonstrate that the proposed asynchronous federated deep learning outperforms the baseline algorithm both in terms of communication cost and model accuracy.

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Quality-Gated Convolutional LSTM for Enhancing Compressed Video

Apr 14, 2019
Ren Yang, Xiaoyan Sun, Mai Xu, Wenjun Zeng

The past decade has witnessed great success in applying deep learning to enhance the quality of compressed video. However, the existing approaches aim at quality enhancement on a single frame, or only using fixed neighboring frames. Thus they fail to take full advantage of the inter-frame correlation in the video. This paper proposes the Quality-Gated Convolutional Long Short-Term Memory (QG-ConvLSTM) network with bi-directional recurrent structure to fully exploit the advantageous information in a large range of frames. More importantly, due to the obvious quality fluctuation among compressed frames, higher quality frames can provide more useful information for other frames to enhance quality. Therefore, we propose learning the "forget" and "input" gates in the ConvLSTM cell from quality-related features. As such, the frames with various quality contribute to the memory in ConvLSTM with different importance, making the information of each frame reasonably and adequately used. Finally, the experiments validate the effectiveness of our QG-ConvLSTM approach in advancing the state-of-the-art quality enhancement of compressed video, and the ablation study shows that our QG-ConvLSTM approach is learnt to make a trade-off between quality and correlation when leveraging multi-frame information. The project page:

* Accepted to IEEE International Conference on Multimedia and Expo (ICME) 2019 

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Photo Stylistic Brush: Robust Style Transfer via Superpixel-Based Bipartite Graph

Jul 15, 2016
Jiaying Liu, Wenhan Yang, Xiaoyan Sun, Wenjun Zeng

With the rapid development of social network and multimedia technology, customized image and video stylization has been widely used for various social-media applications. In this paper, we explore the problem of exemplar-based photo style transfer, which provides a flexible and convenient way to invoke fantastic visual impression. Rather than investigating some fixed artistic patterns to represent certain styles as was done in some previous works, our work emphasizes styles related to a series of visual effects in the photograph, e.g. color, tone, and contrast. We propose a photo stylistic brush, an automatic robust style transfer approach based on Superpixel-based BIpartite Graph (SuperBIG). A two-step bipartite graph algorithm with different granularity levels is employed to aggregate pixels into superpixels and find their correspondences. In the first step, with the extracted hierarchical features, a bipartite graph is constructed to describe the content similarity for pixel partition to produce superpixels. In the second step, superpixels in the input/reference image are rematched to form a new superpixel-based bipartite graph, and superpixel-level correspondences are generated by a bipartite matching. Finally, the refined correspondence guides SuperBIG to perform the transformation in a decorrelated color space. Extensive experimental results demonstrate the effectiveness and robustness of the proposed method for transferring various styles of exemplar images, even for some challenging cases, such as night images.

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Deep Stacking Networks for Low-Resource Chinese Word Segmentation with Transfer Learning

Nov 04, 2017
Jingjing Xu, Xu Sun, Sujian Li, Xiaoyan Cai, Bingzhen Wei

In recent years, neural networks have proven to be effective in Chinese word segmentation. However, this promising performance relies on large-scale training data. Neural networks with conventional architectures cannot achieve the desired results in low-resource datasets due to the lack of labelled training data. In this paper, we propose a deep stacking framework to improve the performance on word segmentation tasks with insufficient data by integrating datasets from diverse domains. Our framework consists of two parts, domain-based models and deep stacking networks. The domain-based models are used to learn knowledge from different datasets. The deep stacking networks are designed to integrate domain-based models. To reduce model conflicts, we innovatively add communication paths among models and design various structures of deep stacking networks, including Gaussian-based Stacking Networks, Concatenate-based Stacking Networks, Sequence-based Stacking Networks and Tree-based Stacking Networks. We conduct experiments on six low-resource datasets from various domains. Our proposed framework shows significant performance improvements on all datasets compared with several strong baselines.

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MARLow: A Joint Multiplanar Autoregressive and Low-Rank Approach for Image Completion

Jun 16, 2016
Mading Li, Jiaying Liu, Zhiwei Xiong, Xiaoyan Sun, Zongming Guo

In this paper, we propose a novel multiplanar autoregressive (AR) model to exploit the correlation in cross-dimensional planes of a similar patch group collected in an image, which has long been neglected by previous AR models. On that basis, we then present a joint multiplanar AR and low-rank based approach (MARLow) for image completion from random sampling, which exploits the nonlocal self-similarity within natural images more effectively. Specifically, the multiplanar AR model constraints the local stationarity in different cross-sections of the patch group, while the low-rank minimization captures the intrinsic coherence of nonlocal patches. The proposed approach can be readily extended to multichannel images (e.g. color images), by simultaneously considering the correlation in different channels. Experimental results demonstrate that the proposed approach significantly outperforms state-of-the-art methods, even if the pixel missing rate is as high as 90%.

* 16 pages, 9 figures 

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One-Shot Neural Architecture Search Through A Posteriori Distribution Guided Sampling

Jun 23, 2019
Yizhou Zhou, Xiaoyan Sun, Chong Luo, Zheng-Jun Zha, Wenjun Zeng

The emergence of one-shot approaches has greatly advanced the research on neural architecture search (NAS). Recent approaches train an over-parameterized super-network (one-shot model) and then sample and evaluate a number of sub-networks, which inherit weights from the one-shot model. The overall searching cost is significantly reduced as training is avoided for sub-networks. However, the network sampling process is casually treated and the inherited weights from an independently trained super-network perform sub-optimally for sub-networks. In this paper, we propose a novel one-shot NAS scheme to address the above issues. The key innovation is to explicitly estimate the joint a posteriori distribution over network architecture and weights, and sample networks for evaluation according to it. This brings two benefits. First, network sampling under the guidance of a posteriori probability is more efficient than conventional random or uniform sampling. Second, the network architecture and its weights are sampled as a pair to alleviate the sub-optimal weights problem. Note that estimating the joint a posteriori distribution is not a trivial problem. By adopting variational methods and introducing a hybrid network representation, we convert the distribution approximation problem into an end-to-end neural network training problem which is neatly approached by variational dropout. As a result, the proposed method reduces the number of sampled sub-networks by orders of magnitude. We validate our method on the fundamental image classification task. Results on Cifar-10, Cifar-100 and ImageNet show that our method strikes the best trade-off between precision and speed among NAS methods. On Cifar-10, we speed up the searching process by 20x and achieve a higher precision than the best network found by existing NAS methods.

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A Skeleton-Based Model for Promoting Coherence Among Sentences in Narrative Story Generation

Aug 27, 2018
Jingjing Xu, Xuancheng Ren, Yi Zhang, Qi Zeng, Xiaoyan Cai, Xu Sun

Narrative story generation is a challenging problem because it demands the generated sentences with tight semantic connections, which has not been well studied by most existing generative models. To address this problem, we propose a skeleton-based model to promote the coherence of generated stories. Different from traditional models that generate a complete sentence at a stroke, the proposed model first generates the most critical phrases, called skeleton, and then expands the skeleton to a complete and fluent sentence. The skeleton is not manually defined, but learned by a reinforcement learning method. Compared to the state-of-the-art models, our skeleton-based model can generate significantly more coherent text according to human evaluation and automatic evaluation. The G-score is improved by 20.1% in the human evaluation. The code is available at

* Accepted by EMNLP 2018 

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Temporal-Spatial Mapping for Action Recognition

Sep 11, 2018
Xiaolin Song, Cuiling Lan, Wenjun Zeng, Junliang Xing, Jingyu Yang, Xiaoyan Sun

Deep learning models have enjoyed great success for image related computer vision tasks like image classification and object detection. For video related tasks like human action recognition, however, the advancements are not as significant yet. The main challenge is the lack of effective and efficient models in modeling the rich temporal spatial information in a video. We introduce a simple yet effective operation, termed Temporal-Spatial Mapping (TSM), for capturing the temporal evolution of the frames by jointly analyzing all the frames of a video. We propose a video level 2D feature representation by transforming the convolutional features of all frames to a 2D feature map, referred to as VideoMap. With each row being the vectorized feature representation of a frame, the temporal-spatial features are compactly represented, while the temporal dynamic evolution is also well embedded. Based on the VideoMap representation, we further propose a temporal attention model within a shallow convolutional neural network to efficiently exploit the temporal-spatial dynamics. The experiment results show that the proposed scheme achieves the state-of-the-art performance, with 4.2% accuracy gain over Temporal Segment Network (TSN), a competing baseline method, on the challenging human action benchmark dataset HMDB51.

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Regularizing Output Distribution of Abstractive Chinese Social Media Text Summarization for Improved Semantic Consistency

May 10, 2018
Bingzhen Wei, Xuancheng Ren, Xu Sun, Yi Zhang, Xiaoyan Cai, Qi Su

Abstractive text summarization is a highly difficult problem, and the sequence-to-sequence model has shown success in improving the performance on the task. However, the generated summaries are often inconsistent with the source content in semantics. In such cases, when generating summaries, the model selects semantically unrelated words with respect to the source content as the most probable output. The problem can be attributed to heuristically constructed training data, where summaries can be unrelated to the source content, thus containing semantically unrelated words and spurious word correspondence. In this paper, we propose a regularization approach for the sequence-to-sequence model and make use of what the model has learned to regularize the learning objective to alleviate the effect of the problem. In addition, we propose a practical human evaluation method to address the problem that the existing automatic evaluation method does not evaluate the semantic consistency with the source content properly. Experimental results demonstrate the effectiveness of the proposed approach, which outperforms almost all the existing models. Especially, the proposed approach improves the semantic consistency by 4\% in terms of human evaluation.

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The Deep Learning Compiler: A Comprehensive Survey

Feb 27, 2020
Mingzhen Li, Yi Liu, Xiaoyan Liu, Qingxiao Sun, Xin You, Hailong Yang, Zhongzhi Luan, Depei Qian

The difficulty of deploying various deep learning (DL) models on diverse DL hardware has boosted the research and development of DL compilers in the community. Several DL compilers have been proposed from both industry and academia such as Tensorflow XLA and TVM. Similarly, the DL compilers take the DL models described in different DL frameworks as input, and then generate optimized codes for diverse DL hardware as output. However, none of the existing survey has analyzed the unique design of the DL compilers comprehensively. In this paper, we perform a comprehensive survey of existing DL compilers by dissecting the commonly adopted design in details, with emphasis on the DL oriented multi-level IRs, and frontend/backend optimizations. Specifically, we provide a comprehensive comparison among existing DL compilers from various aspects. In addition, we present detailed analysis of the multi-level IR design and compiler optimization techniques. Finally, several insights are highlighted as the potential research directions of DL compiler. This is the first survey paper focusing on the unique design of DL compiler, which we hope can pave the road for future research towards the DL compiler.

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Dual Learning-based Video Coding with Inception Dense Blocks

Nov 22, 2019
Chao Liu, Heming Sun, Junan Chen, Zhengxue Cheng, Masaru Takeuchi, Jiro Katto, Xiaoyang Zeng, Yibo Fan

In this paper, a dual learning-based method in intra coding is introduced for PCS Grand Challenge. This method is mainly composed of two parts: intra prediction and reconstruction filtering. They use different network structures, the neural network-based intra prediction uses the full-connected network to predict the block while the neural network-based reconstruction filtering utilizes the convolutional networks. Different with the previous filtering works, we use a network with more powerful feature extraction capabilities in our reconstruction filtering network. And the filtering unit is the block-level so as to achieve a more accurate filtering compensation. To our best knowledge, among all the learning-based methods, this is the first attempt to combine two different networks in one application, and we achieve the state-of-the-art performance for AI configuration on the HEVC Test sequences. The experimental result shows that our method leads to significant BD-rate saving for provided 8 sequences compared to HM-16.20 baseline (average 10.24% and 3.57% bitrate reductions for all-intra and random-access coding, respectively). For HEVC test sequences, our model also achieved a 9.70% BD-rate saving compared to HM-16.20 baseline for all-intra configuration.

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