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

Most existing object instance detection and segmentation models only work well on fairly balanced benchmarks where per-category training sample numbers are comparable, such as COCO. They tend to suffer performance drop on realistic datasets that are usually long-tailed. This work aims to study and address such open challenges. Specifically, we systematically investigate performance drop of the state-of-the-art two-stage instance segmentation model Mask R-CNN on the recent long-tail LVIS dataset, and unveil that a major cause is the inaccurate classification of object proposals. Based on such an observation, we first consider various techniques for improving long-tail classification performance which indeed enhance instance segmentation results. We then propose a simple calibration framework to more effectively alleviate classification head bias with a bi-level class balanced sampling approach. Without bells and whistles, it significantly boosts the performance of instance segmentation for tail classes on the recent LVIS dataset and our sampled COCO-LT dataset. Our analysis provides useful insights for solving long-tail instance detection and segmentation problems, and the straightforward SimCal method can serve as a simple but strong baseline. With the method we have won the 2019 LVIS challenge. Codes and models are available at https://github.com/twangnh/SimCal.

The Devil Is in Classification: A Simple Framework for Long-Tail Instance Segmentation

We present PornProbe, a pornography detection system that detects pornographic contents in videos. To build such a detection system, we leverage a large scale training data set with 65,827 positive training image samples out of a total of 420,615 training samples, and a novel detection scheme based on hierarchical LDA-SVM. The system combines the unsupervised clustering in Latent Dirichlet Allocation (LDA) and supervised learning in Support Vector Machine, so as to achieve both high precision and recall while ensuring efficiency in both training and testing. This demonstration shows how the system detects the pornographic scenes in restricted artistic (RA) movies.

Pornprobe: an LDA-SVM based pornography detection system

Logo detection is important for brand advertising and surveillance applications. The central issues of this technology are fast localization and accurate matching. Based on key traits analysis of common logos, this paper presents a two-stage detection scheme based on spatialspectral saliency (SSS) and partial spatial context (PSC). SSS speeds up logo location and avoid the impact of cluttered background. PSC filters false matching using spatial consistency of local invariant points. The integration of SSS and PSC result in faster localization and increased accuracy. Experiments on a dataset of nearly 10,000 web images containing several popular logo types are presented. The results indicate that our method is applicable and precise for different logo detection scenarios.

Logo detection based on spatial-spectral saliency and partial spatial context

Zero-shot learning (ZSL) is regarded as an effective way to construct classification models for target classes which have no labeled samples available. The basic framework is to transfer knowledge from (different) auxiliary source classes having sufficient labeled samples with some attributes shared by target and source classes as bridge. Attributes play an important role in ZSL but they have not gained sufficient attention in recent years. Previous works mostly assume attributes are perfect and treat each attribute equally. However, as shown in this paper, different attributes have different properties, such as their class distribution, variance, and entropy, which may have considerable impact on ZSL accuracy if treated equally. Based on this observation, in this paper we propose to use a subset of attributes, instead of the whole set, for building ZSL models. The attribute selection is conducted by considering the information amount and predictability under a novel joint optimization framework. To our knowledge, this is the first work that notices the influence of attributes themselves and proposes to use a refined attribute set for ZSL. Since our approach focuses on selecting good attributes for ZSL, it can be combined to any attribute based ZSL approaches so as to augment their performance. Experiments on four ZSL benchmarks demonstrate that our approach can improve zeroshot classification accuracy and yield state-of-the-art results. Introduction Image classification, whose goal is to identify the category of instances in an image, is an active research topic in machine learning and computer vision communities. Recently, benefiting from the fast development of deep learning techniques (Krizhevsky, Sutskever, and Hinton 2012; Simonyan and Zisserman 2014; He et al. 2016; Huang et al. 2016), the image classification accuracy on many benchmarks, including the large-scale ImageNet (Russakovsky et al. 2015), has been improved tremendously and even surpassed human-level performance. It should be noticed that the progress in image classification relies heavily on a largescale training set which provides sufficient labeled samples ∗This research was supported by the National Natural Science Foundation of China (Grant No. 61571269) and the Royal Society Newton Mobility Grant (IE150997). Corresponding author: Guiguang Ding. Copyright c © 2018, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. !" # "

Sheng Tang

Papers

The Devil Is in Classification: A Simple Framework for Long-Tail Instance Segmentation

Pornprobe: an LDA-SVM based pornography detection system

Logo detection based on spatial-spectral saliency and partial spatial context

Zero-Shot Learning With Attribute Selection.

Robust common visual pattern discovery using graph matching