A Reverse Hierarchy Model for Predicting Eye Fixations

A number of psychological and physiological evidences suggest that early visual attention works in a coarse-to-fine way, which lays a basis for the reverse hierarchy theory (RHT). This theory states that attention propagates from the top level of the visual hierarchy that processes gist and abstract information of input, to the bottom level that processes local details. Inspired by the theory, we develop a computational model for saliency detection in images. First, the original image is downsampled to different scales to constitute a pyramid. Then, saliency on each layer is obtained by image super-resolution reconstruction from the layer above, which is defined as unpredictability from this coarse-to-fine reconstruction. Finally, saliency on each layer of the pyramid is fused into stochastic fixations through a probabilistic model, where attention initiates from the top layer and propagates downward through the pyramid. Extensive experiments on two standard eye-tracking datasets show that the proposed method can achieve competitive results with state-of-the-art models.Comment: CVPR 2014, 27th IEEE Conference on Computer Vision and Pattern Recognition (CVPR). CVPR 201

Defense against Adversarial Attacks Using High-Level Representation Guided Denoiser

Neural networks are vulnerable to adversarial examples, which poses a threat to their application in security sensitive systems. We propose high-level representation guided denoiser (HGD) as a defense for image classification. Standard denoiser suffers from the error amplification effect, in which small residual adversarial noise is progressively amplified and leads to wrong classifications. HGD overcomes this problem by using a loss function defined as the difference between the target model's outputs activated by the clean image and denoised image. Compared with ensemble adversarial training which is the state-of-the-art defending method on large images, HGD has three advantages. First, with HGD as a defense, the target model is more robust to either white-box or black-box adversarial attacks. Second, HGD can be trained on a small subset of the images and generalizes well to other images and unseen classes. Third, HGD can be transferred to defend models other than the one guiding it. In NIPS competition on defense against adversarial attacks, our HGD solution won the first place and outperformed other models by a large margin

3DCFS : Fast and robust joint 3D semantic-instance segmentation via coupled feature selection

We propose a novel fast and robust 3D point clouds segmentation framework via coupled feature selection, named 3DCFS, that jointly performs semantic and instance segmentation. Inspired by the human scene perception process, we design a novel coupled feature selection module, named CFSM, that adaptively selects and fuses the reciprocal semantic and instance features from two tasks in a coupled manner. To further boost the performance of the instance segmentation task in our 3DCFS, we investigate a loss function that helps the model learn to balance the magnitudes of the output embedding dimensions during training, which makes calculating the Euclidean distance more reliable and enhances the generalizability of the model. Extensive experiments demonstrate that our 3DCFS outperforms state-of-the-art methods on benchmark datasets in terms of accuracy, speed and computational cost

An Experimental Study on Effect of Steel Corrosion on the Bond–Slip Performance of Reinforced Concrete

This paper studied the effects of reinforcement corrosion on bond performance between rebar and concrete. Tests were carried out to evaluate the degradation of bond between reinforcing steel and concrete for different corrosion levels of reinforcing steel. A series of 20 specimens of different concrete strength with various reinforcing steel corrosion levels were designed and manufactured. Each specimen was casted as a 200-mm concrete cube, and a steel rebar was centrally embedded with two stirrups around it. The steel rebar were corroded using an electrochemical accelerated corrosion technique. The corrosion crack opening width and length were recorded after the corrosion process. Then, monolithic pull-out loading tests were carried out on the specimens. The effects of reinforcement corrosion on crack opening, maximum bond stress, and energy dissipation were discussed in detail. It was found that reinforcement corrosion has non-negligible effects on bond performance of reinforcing bar in concrete

Research on the Impact of Information Quality on Educational WeChat Official Account Users’ Continued Use Intention: Based on the ECM-IS Model

Along with the vigorous development of WeChat official accounts, the number of education WeChat official accounts is increasing day by day. Society’s emphasis on education issues extends to the WeChat Official Accounts and other social networking platforms. How to attract users and maintain the willingness of users to continue to use is an important issue for the operators of such public accounts to consider. Information is the main content of the educational WeChat official account, and the evaluation of its quality will directly affect the perception of the educational WeChat official account and then affect the user\u27s willingness to use it. Therefore, based on the perspective of information quality and the ECM-IS model as the research model, this paper subdivides information quality into five dimensions, including comprehensiveness, timeliness, accuracy, relevance, and perceived interestingness, and establishes a theoretical model of the influence of educational WeChat public account information quality on college students\u27 willingness to continue using. This study will have a certain reference value for the education of WeChat operators\u27 practical operation and user stickiness