arXiv subjects

Yutong Xie

Publications and source records attributed to Yutong Xie.

112 records · Page 7Linked to original sources

arXiv2019

Visual Rhythm Prediction with Feature-Aligning Network

In this paper, we propose a data-driven visual rhythm prediction method, which overcomes the previous works' deficiency that predictions are made primarily by human-crafted hard rules. In our approach, we first extract features including original frames and their residuals, optical flow, scene change, and body pose. These visual features will be next taken into an end-to-end neural network as inputs. Here we observe that there are some slight misaligning between features over the timeline and assume that this is due to the distinctions between how different features are computed. To solve this problem, the extracted features are aligned by an elaborately designed layer, which can also be applied to other models suffering from mismatched features, and boost performance. Then these aligned features are fed into sequence labeling layers implemented with BiLSTM and CRF to predict the onsets. Due to the lack of existing public training and evaluation set, we experiment on a dataset constructed by ourselves based on professionally edited Music Videos (MVs), and the F1 score of our approach reaches 79.6.

cs.CV↗

arXiv2018

A Multi-Level Deep Ensemble Model for Skin Lesion Classification in Dermoscopy Images

A multi-level deep ensemble (MLDE) model that can be trained in an 'end to end' manner is proposed for skin lesion classification in dermoscopy images. In this model, four pre-trained ResNet-50 networks are used to characterize the multiscale information of skin lesions and are combined by using an adaptive weighting scheme that can be learned during the error back propagation. The proposed MLDE model achieved an average AUC value of 86.5% on the ISIC-skin 2018 official validation dataset, which is substantially higher than the average AUC values achieved by each of four ResNet-50 networks.

cs.CV↗

arXiv2017

Classification of Medical Images and Illustrations in the Biomedical Literature Using Synergic Deep Learning

The Classification of medical images and illustrations in the literature aims to label a medical image according to the modality it was produced or label an illustration according to its production attributes. It is an essential and challenging research hotspot in the area of automated literature review, retrieval and mining. The significant intra-class variation and inter-class similarity caused by the diverse imaging modalities and various illustration types brings a great deal of difficulties to the problem. In this paper, we propose a synergic deep learning (SDL) model to address this issue. Specifically, a dual deep convolutional neural network with a synergic signal system is designed to mutually learn image representation. The synergic signal is used to verify whether the input image pair belongs to the same category and to give the corrective feedback if a synergic error exists. Our SDL model can be trained 'end to end'. In the test phase, the class label of an input can be predicted by averaging the likelihood probabilities obtained by two convolutional neural network components. Experimental results on the ImageCLEF2016 Subfigure Classification Challenge suggest that our proposed SDL model achieves the state-of-the art performance in this medical image classification problem and its accuracy is higher than that of the first place solution on the Challenge leader board so far.

cs.CV↗

arXiv2015

Spot size measurement of flash-radiography source utilizing the pinhole imaging method

The spot size of the x-ray source is a key parameter of a flash-radiography facility, which is usually quoted as an evaluation of the resolving power. The pinhole imaging technique is applied to measure the spot size of the Dragon-I linear induction accelerator, by which a two-dimensional spatial distribution of the source spot is obtained. Experimental measurements are performed to measure the spot image when the transportation and focusing of the electron beam is tuned by adjusting the currents of solenoids in the downstream section. The spot size of full-width at half maximum and that defined from the spatial frequency at half peak value of the modulation transfer function are calculated and discussed.

physics.acc-ph↗