Deep Spatio-Temporal Random Fields for Efficient Video Segmentation.

In this work we introduce a time- and memory-efficient method for structured prediction that couples neuron decisions across both space at time. We show that we are able to perform exact and efficient inference on a densely-connected spatio-temporal graph by capitalizing on recent advances on deep Gaussian Conditional Random Fields (GCRFs). Our method, called VideoGCRF is (a) efficient, (b) has a unique global minimum, and (c) can be trained end-to-end alongside contemporary deep networks for video understanding. We experiment with multiple connectivity patterns in the temporal domain, and present empirical improvements over strong baselines on the tasks of both semantic and instance segmentation of videos. Our implementation is based on the Caffe2 framework and will be available at https://github.com/siddharthachandra/gcrf-v3.0

Deeper Image Quality Transfer: Training Low-Memory Neural Networks for 3D Images

In this paper we address the memory demands that come with the processing of 3-dimensional, high-resolution, multi-channeled medical images in deep learning. We exploit memory-efficient backpropagation techniques, to reduce the memory complexity of network training from being linear in the network's depth, to being roughly constant $-$ permitting us to elongate deep architectures with negligible memory increase. We evaluate our methodology in the paradigm of Image Quality Transfer, whilst noting its potential application to various tasks that use deep learning. We study the impact of depth on accuracy and show that deeper models have more predictive power, which may exploit larger training sets. We obtain substantially better results than the previous state-of-the-art model with a slight memory increase, reducing the root-mean-squared-error by $13\%$. Our code is publicly available.Comment: Accepted in: MICCAI 201

Impact of a stress management program on weight loss, mental health and lifestyle in adults with obesity: a randomized controlled trial

Aim: To evaluate the impact of a stress management program on weight loss, depression, anxiety and stress as well as on the adoption of healthy lifestyle in adults with obesity. Methods: Adults with obesity who sought help for weight loss at a medical obesity clinic were consecutively enrolled in the study and were randomly assigned to the intervention or control group. All participants received standard instructions for a healthy lifestyle. The intervention group attended an 8-week stress management program that comprised diaphragmatic breathing, progressive muscle relaxation, guided visualization and instructions about healthy nutrition/dietary habits. Anthropometric parameters were assessed and several questionnaires were completed by all participants, at the beginning and at the end of the study. Results: A total of 45 adults (mean age±SD 45.7±10.55 years) with obesity were enrolled in the study; 22 in the intervention group and 23 in the control group. Participants in the two groups were matched for age and BMI. Participants in the intervention group achieved a significantly larger reduction in BMI compared to the control group (ΔBMI -3.1 vs. -1.74 kg/m2 respectively, P<0.001). In addition, they displayed ameliorated depression and anxiety scores and a reduction in the health locus of control based on chance

A Graph Theoretic Approach for Object Shape Representation in Compositional Hierarchies Using a Hybrid Generative-Descriptive Model

A graph theoretic approach is proposed for object shape representation in a hierarchical compositional architecture called Compositional Hierarchy of Parts (CHOP). In the proposed approach, vocabulary learning is performed using a hybrid generative-descriptive model. First, statistical relationships between parts are learned using a Minimum Conditional Entropy Clustering algorithm. Then, selection of descriptive parts is defined as a frequent subgraph discovery problem, and solved using a Minimum Description Length (MDL) principle. Finally, part compositions are constructed by compressing the internal data representation with discovered substructures. Shape representation and computational complexity properties of the proposed approach and algorithms are examined using six benchmark two-dimensional shape image datasets. Experiments show that CHOP can employ part shareability and indexing mechanisms for fast inference of part compositions using learned shape vocabularies. Additionally, CHOP provides better shape retrieval performance than the state-of-the-art shape retrieval methods.Comment: Paper : 17 pages. 13th European Conference on Computer Vision (ECCV 2014), Zurich, Switzerland, September 6-12, 2014, Proceedings, Part III, pp 566-581. Supplementary material can be downloaded from http://link.springer.com/content/esm/chp:10.1007/978-3-319-10578-9_37/file/MediaObjects/978-3-319-10578-9_37_MOESM1_ESM.pd

DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs

In this work we address the task of semantic image segmentation with Deep Learning and make three main contributions that are experimentally shown to have substantial practical merit. First, we highlight convolution with upsampled filters, or `atrous convolution', as a powerful tool in dense prediction tasks. Atrous convolution allows us to explicitly control the resolution at which feature responses are computed within Deep Convolutional Neural Networks. It also allows us to effectively enlarge the field of view of filters to incorporate larger context without increasing the number of parameters or the amount of computation. Second, we propose atrous spatial pyramid pooling (ASPP) to robustly segment objects at multiple scales. ASPP probes an incoming convolutional feature layer with filters at multiple sampling rates and effective fields-of-views, thus capturing objects as well as image context at multiple scales. Third, we improve the localization of object boundaries by combining methods from DCNNs and probabilistic graphical models. The commonly deployed combination of max-pooling and downsampling in DCNNs achieves invariance but has a toll on localization accuracy. We overcome this by combining the responses at the final DCNN layer with a fully connected Conditional Random Field (CRF), which is shown both qualitatively and quantitatively to improve localization performance. Our proposed “DeepLab” system sets the new state-of-art at the PASCAL VOC-2012 semantic image segmentation task, reaching 79.7 percent mIOU in the test set, and advances the results on three other datasets: PASCAL-Context, PASCAL-Person-Part, and Cityscapes. All of our code is made publicly available online

Relationship of physical activity and healthy eating with mortality and incident heart failure among community-dwelling older adults with normal body mass index

Aims Normal body mass index (BMI) is associated with lower mortality and may be achieved by physical activity (PA), healthy eating (HE), or both. We examined the association of PA and HE with mortality and incident heart failure (HF) among 2040 community-dwelling older adults aged ≥ 65 years with baseline BMI 18.5 to 24.99 kg/m2 during 13 years of follow-up in the Cardiovascular Health Study. Methods and results Baseline PA was defined as ≥500 weekly metabolic equivalent task-minutes and HE as ≥5 daily servings of vegetable and fruit intake. Participants were categorized into four groups: (i) PA−/HE− (n = 384); (ii) PA−/HE+ (n = 162); (iii) PA+/HE− (n = 992); and (iv) PA+/HE+ (n = 502). Participants had a mean age of 74 (±6) years, mean BMI of 22.6 (±1.5) kg/m2, 61% were women, and 4% African American. Compared with PA−/HE−, age-sex-race-adjusted hazard ratios and 95% confidence intervals for all-cause mortality for PA−/HE+, PA+/HE−, and PA+/HE+ groups were 0.96 (0.76–1.21), 0.61 (0.52–0.71), and 0.62 (0.52–0.75), respectively. These associations remained unchanged after multivariable adjustment and were similar for cardiovascular and non-cardiovascular mortalities. Respective demographic-adjusted hazard ratios (95% confidence intervals) for incident HF among 1954 participants without baseline HF were 1.21 (0.81–1.81), 0.71 (0.54–0.94), and 0.71 (0.51–0.98). These latter associations lost significance after multivariable adjustment. Conclusion Among community-dwelling older adults with normal BMI, physical activity, regardless of healthy eating, was associated with lower risk of mortality and incident HF, but healthy eating had no similar protective association in this cohort

Object Detection Through Exploration With A Foveated Visual Field

We present a foveated object detector (FOD) as a biologically-inspired alternative to the sliding window (SW) approach which is the dominant method of search in computer vision object detection. Similar to the human visual system, the FOD has higher resolution at the fovea and lower resolution at the visual periphery. Consequently, more computational resources are allocated at the fovea and relatively fewer at the periphery. The FOD processes the entire scene, uses retino-specific object detection classifiers to guide eye movements, aligns its fovea with regions of interest in the input image and integrates observations across multiple fixations. Our approach combines modern object detectors from computer vision with a recent model of peripheral pooling regions found at the V1 layer of the human visual system. We assessed various eye movement strategies on the PASCAL VOC 2007 dataset and show that the FOD performs on par with the SW detector while bringing significant computational cost savings.Comment: An extended version of this manuscript was published in PLOS Computational Biology (October 2017) at https://doi.org/10.1371/journal.pcbi.100574