다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

In this work, we revisit atrous convolution, a powerful tool to explicitly adjust filter's field-of-view as well as control the resolution of feature responses computed by Deep Convolutional Neural Networks, in the application of semantic image segmentation. To handle the problem of segmenting objects at multiple scales, we design modules which employ atrous convolution in cascade or in parallel to capture multi-scale context by adopting multiple atrous rates. Furthermore, we propose to augment our previously proposed Atrous Spatial Pyramid Pooling module, which probes convolutional features at multiple scales, with image-level features encoding global context and further boost performance. We also elaborate on implementation details and share our experience on training our system. The proposed `DeepLabv3' system significantly improves over our previous DeepLab versions without DenseCRF post-processing and attains comparable performance with other state-of-art models on the PASCAL VOC 2012 semantic image segmentation benchmark.

Rethinking Atrous Convolution for Semantic Image Segmentation

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

The PASCAL Visual Object Classes Challenge

Semantic segmentation requires both rich spatial information and sizeable receptive field. However, modern approaches usually compromise spatial resolution to achieve real-time inference speed, which leads to poor performance. In this paper, we address this dilemma with a novel Bilateral Segmentation Network (BiSeNet). We first design a Spatial Path with a small stride to preserve the spatial information and generate high-resolution features. Meanwhile, a Context Path with a fast downsampling strategy is employed to obtain sufficient receptive field. On top of the two paths, we introduce a new Feature Fusion Module to combine features efficiently. The proposed architecture makes a right balance between the speed and segmentation performance on Cityscapes, CamVid, and COCO-Stuff datasets. Specifically, for a 2048 \(\times \) 1024 input, we achieve 68.4% Mean IOU on the Cityscapes test dataset with speed of 105 FPS on one NVIDIA Titan XP card, which is significantly faster than the existing methods with comparable performance.

/pdf/bisenet-bilateral-segmentation-network-for-real-time-2pc7rh0tw1.pdf

BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation

Typical approaches to multi-label image classification learn a binary classifier for each label, including the binary cross-entropy (CE) loss function in convolutional neural network (CNN) based methods, which learns each label with independent binary logistic regression at the output layer. While these approaches have achieved sufficient success due to the strong learning capability of CNN, further improvements are limited for their neglect of the data imbalance and failure in exploiting explicit label correlations. In this paper, we deal with this problem by jointly learning the binary classifiers and pairwise label correlations (JBP) in an end-to-end manner. For pairwise learning, we introduce the strategy of online hard sample mining to focus on distinguishing confusing label pairs. we also investigate an imbalance aware cross-entropy (ICE) loss function by incorporating cost-sensitivity into the existing cross-entropy loss. Experiments on three popular datasets demonstrate the effectiveness of our proposed method.

Joint Learning of Binary Classifiers and Pairwise Label Correlations for Multi-label Image Classification

In this paper, we specially propose a hierarchical framework for movie content analysis. The purpose of our work is trying to realize computerspsila understanding for movie content, especially ldquowho, what, where, howrdquo which occur in the storyline by imitating human perception and cognition. The framework consists of two hierarchies. As for the low level part, we originally construct the human attention model with temporal information motivated by the Weber-Fechner Law to depict the variation of human perception in multiple modalities. As for the high level part, we focus on semantic understanding of different granularities of videos and simulate human cognition for movie content. Based on this hierarchical framework, we present its applications on semantic retrieval, video summarization and content filter. The promising results of userspsila subjective assessment indicate that the proposed framework is applicable for automatic analysis of movie content by computers.

A hierarchical framework for movie content analysis: Let computers watch films like humans

https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=4995&context=sis_research

FSpH: Fitted spectral hashing for efficient similarity search

Representative selection based on sparse modeling

In this paper, we present a novel scalable logo recognition system which can recognize a large number of logo categories locally on mobile devices. The system is unsupervised without any supervised training procedure, and very time efficient at low memory cost. It is also robust against challenging conditions such as noise addition, different image scale, rotation, etc. To achieve this goal, we propose an efficient segmental quantization approach for generation of large visual words over one million size with a very compact vocabulary. The vocabulary consists of two small dictionaries learned through sparse non-negative matrix factorization (NMF) of local SIFT descriptors. With an inverted index structure built through the large visual words, query images containing logos can be recognized through efficient retrieval of K-nearest neighbors (K-NN) of logo instances in the dataset. Our vocabulary size is very small, only one thousandth of that of traditional Approximate K-Means (AKM) method, which is of great importance for mobile devices with limited memory. Furthermore, based on the compact dictionary, we present a promising verification way of filtering false positives via sparse reconstruction of SIFT descriptors with a very few number of sparse codes due to the sparsity's property of lowest reconstruction error. Experiments on our dataset with 400 logo classes show that our system is very efficient and effective.

Sheng Tang

Papers

Joint Learning of Binary Classifiers and Pairwise Label Correlations for Multi-label Image Classification

A hierarchical framework for movie content analysis: Let computers watch films like humans

FSpH: Fitted spectral hashing for efficient similarity search

Representative selection based on sparse modeling

Scalable logo recognition based on compact sparse dictionary for mobile devices