3,018 research outputs found
Accuracy Booster: Performance Boosting using Feature Map Re-calibration
Convolution Neural Networks (CNN) have been extremely successful in solving
intensive computer vision tasks. The convolutional filters used in CNNs have
played a major role in this success, by extracting useful features from the
inputs. Recently researchers have tried to boost the performance of CNNs by
re-calibrating the feature maps produced by these filters, e.g.,
Squeeze-and-Excitation Networks (SENets). These approaches have achieved better
performance by Exciting up the important channels or feature maps while
diminishing the rest. However, in the process, architectural complexity has
increased. We propose an architectural block that introduces much lower
complexity than the existing methods of CNN performance boosting while
performing significantly better than them. We carry out experiments on the
CIFAR, ImageNet and MS-COCO datasets, and show that the proposed block can
challenge the state-of-the-art results. Our method boosts the ResNet-50
architecture to perform comparably to the ResNet-152 architecture, which is a
three times deeper network, on classification. We also show experimentally that
our method is not limited to classification but also generalizes well to other
tasks such as object detection.Comment: IEEE Winter Conference on Applications of Computer Vision (WACV),
202
Fiji East Indian educational implications in Monterey County
This research examines the cultural and economical background of East Indian indentured laborers in the Fiji Islands. It takes a view of the living conditions during and after their five year indentured labor contract, and studies the educational implications of these immigrants in Monterey County. Via historical, qualitative methodologies, ethnographic interviews and observations, and review of relevent literature, this research discovers the many educational problems encountered by the Fiji East Indian students and their families in Monterey County. It analyses the gathered data and suggests ways to facilitate the education of these Fiji East Indian immigrants
Clinical Outcome of a Novel Anti-CD6 Biologic Itolizumab in Patients of Psoriasis with Comorbid Conditions
Psoriasis is a common, chronic, immune mediated, inflammatory disease of skin characterized by red patches enclosed with white scales and affects 2-3% of people in the world. Topical therapy, phototherapy, and systemic therapy were employed for management of disease from many last decades. However, long term uses of these agents are associated with unwanted effects and toxicities. Recently, Itolizumab has been developed as world’s first anti-CD6 humanized monoclonal IgG1 antibody for the management of moderate-to-severe chronic plaque psoriasis in India. Here we are presenting the response indicated by Itolizumab in 7 Indian patients having moderate-to-severe psoriasis with severe comorbidities and who were intolerant/nonresponding to conventional therapies
Minimizing Supervision in Multi-label Categorization
Multiple categories of objects are present in most images. Treating this as a
multi-class classification is not justified. We treat this as a multi-label
classification problem. In this paper, we further aim to minimize the
supervision required for providing supervision in multi-label classification.
Specifically, we investigate an effective class of approaches that associate a
weak localization with each category either in terms of the bounding box or
segmentation mask. Doing so improves the accuracy of multi-label
categorization. The approach we adopt is one of active learning, i.e.,
incrementally selecting a set of samples that need supervision based on the
current model, obtaining supervision for these samples, retraining the model
with the additional set of supervised samples and proceeding again to select
the next set of samples. A crucial concern is the choice of the set of samples.
In doing so, we provide a novel insight, and no specific measure succeeds in
obtaining a consistently improved selection criterion. We, therefore, provide a
selection criterion that consistently improves the overall baseline criterion
by choosing the top k set of samples for a varied set of criteria. Using this
criterion, we are able to show that we can retain more than 98% of the fully
supervised performance with just 20% of samples (and more than 96% using 10%)
of the dataset on PASCAL VOC 2007 and 2012. Also, our proposed approach
consistently outperforms all other baseline metrics for all benchmark datasets
and model combinations.Comment: Accepted in CVPR-W 202
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