segDeepM: Exploiting Segmentation and Context in Deep Neural Networks for Object Detection

In this paper, we propose an approach that exploits object segmentation in order to improve the accuracy of object detection. We frame the problem as inference in a Markov Random Field, in which each detection hypothesis scores object appearance as well as contextual information using Convolutional Neural Networks, and allows the hypothesis to choose and score a segment out of a large pool of accurate object segmentation proposals. This enables the detector to incorporate additional evidence when it is available and thus results in more accurate detections. Our experiments show an improvement of 4.1% in mAP over the R-CNN baseline on PASCAL VOC 2010, and 3.4% over the current state-of-the-art, demonstrating the power of our approach

Skip-Thought Vectors

We describe an approach for unsupervised learning of a generic, distributed sentence encoder. Using the continuity of text from books, we train an encoder-decoder model that tries to reconstruct the surrounding sentences of an encoded passage. Sentences that share semantic and syntactic properties are thus mapped to similar vector representations. We next introduce a simple vocabulary expansion method to encode words that were not seen as part of training, allowing us to expand our vocabulary to a million words. After training our model, we extract and evaluate our vectors with linear models on 8 tasks: semantic relatedness, paraphrase detection, image-sentence ranking, question-type classification and 4 benchmark sentiment and subjectivity datasets. The end result is an off-the-shelf encoder that can produce highly generic sentence representations that are robust and perform well in practice. We will make our encoder publicly available.Comment: 11 page

Spatially Adaptive Computation Time for Residual Networks

This paper proposes a deep learning architecture based on Residual Network that dynamically adjusts the number of executed layers for the regions of the image. This architecture is end-to-end trainable, deterministic and problem-agnostic. It is therefore applicable without any modifications to a wide range of computer vision problems such as image classification, object detection and image segmentation. We present experimental results showing that this model improves the computational efficiency of Residual Networks on the challenging ImageNet classification and COCO object detection datasets. Additionally, we evaluate the computation time maps on the visual saliency dataset cat2000 and find that they correlate surprisingly well with human eye fixation positions.Comment: CVPR 201

Image Deblurring According to Facially Recognized Locations Within the Image

This publication describes techniques for image deblurring according to a facially recognized locations within the image. An algorithm may use facial detection and recognition to selectively sharpen aspects of faces within an image and the surrounding area associated with the facial detection. In one or more aspects, the selectivity of sharpening improves the computational load and other aspects of image provision to improve overall computer function, power consumption, and user experience. Individual faces within the image may be cropped or thumbnailed, providing portions of the image that include the faces. Counterpart images associated with the individual faces may be found within a database having a repository of sharp features associated with the counterpart images. As such, the features may be integrated with the blurred faces of the original image to sharpen an image output

Effects of godet wheel position on compact siro-spun core yarn characteristics

Cotton-spandex compact siro-spun core yarns (29.2tex/44.4dtex and 14.6tex/44.4dtex) have been prepared on two kinds of compact spinning, viz complete condensing spinning system (CCSS) and lattice apron compact spinning system (LACSS) respectively. Three godet wheel positions on two kinds of compact system have been selected and corresponding yarn covering effect is studied respectively. Especially, the surface morphology and cross-sections of the core yarns are observed. Then, the covering effects are compared and affecting factors are analyzed. Moreover, other yarn properties including yarn hairiness, strength and evenness are also tested and compared. The results indicate that the covering effect of staple fibres is the most even when the godet wheel position is set on left side for both CCSS and LACSS

Evolution of the spatiotemporal pattern of PM2.5 concentrations in China – a case study from the Beijing-Tianjin-Hebei region

Atmospheric haze pollution has become a global concern because of its severe effects on human health and the environment. The Beijing-Tianjin-Hebei urban agglomeration is located in northern China, and its haze is the most serious in China. The high concentration of PM2.5 is the main cause of haze pollution, and thus investigating the temporal and spatial characteristics of PM2.5 is important for understanding the mechanisms underlying PM2.5 pollution and for preventing haze. In this study, the PM2.5 concentration status in 13 cities from the Beijing-Tianjin-Hebei region was statistically analyzed from January 2016 to November 2016, and the spatial variation of PM2.5 was explored via spatial autocorrelation analysis. The research yielded three overall results. (1) The distribution of PM2.5 concentrations in this area varied greatly during the study period. The concentrations increased from late autumn to early winter, and the spatial range expanded from southeast to northwest. In contrast, the PM2.5 concentration decreased rapidly from late winter to early spring, and the spatial range narrowed from northwest to southeast. (2) The spatial dependence degree, by season from high to low, was in the order winter, autumn, spring, summer. Winter (from December to February of the subsequent year) and summer (from June to August) were, respectively, the highest and lowest seasons with regard to the spatial homogeneity of PM2.5 concentrations. (3) The PM2.5 concentration in the Beijing-Tianjin-Hebei region has significant spatial spillovers. Overall, cities far from Bohai Bay, such as Shijiazhuang and Hengshui, demonstrated a high-high concentration of PM2.5 pollution, while coastal cities, such as Chengde and Qinhuangdao, showed a low-low concentration