Improving the Accuracy of Genomic Analysis with DeepVariant 1.0
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Read moreAdvancing NLP with Efficient Projection-Based Model Architectures
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PMVOS: Pixel-Level Matching-Based Video Object Segmentation
Semi-supervised video object segmentation (VOS) aims to segment arbitrary target objects in video when the ground truth segmentation mask of the initial frame is provided. Due to this limitation of using prior knowledge about the target object, feature matching, which compares template features representing the target object with input features, is an essential step… Recently, pixel-level matching (PM), which matches every pixel in template features and input features, has been widely used for feature matching because of its high performance. […]
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Face Sketch Synthesis with Style Transfer using Pyramid Column Feature
In this paper, we propose a novel framework based on deep neural networks for face sketch synthesis from a photo. Imitating the process of how artists draw sketches, our framework synthesizes face sketches in a cascaded manner… A content image is first generated that outlines the shape of the face and the key facial features. Textures and shadings are then added to enrich the details of the sketch. We utilize a fully convolutional neural network (FCNN) to create the content […]
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Residual Spatial Attention Network for Retinal Vessel Segmentation
Reliable segmentation of retinal vessels can be employed as a way of monitoring and diagnosing certain diseases, such as diabetes and hypertension, as they affect the retinal vascular structure. In this work, we propose the Residual Spatial Attention Network (RSAN) for retinal vessel segmentation… RSAN employs a modified residual block structure that integrates DropBlock, which can not only be utilized to construct deep networks to extract more complex vascular features, but can also effectively alleviate the overfitting. Moreover, in order […]
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Identification of Abnormal States in Videos of Ants Undergoing Social Phase Change
Biology is both an important application area and a source of motivation for development of advanced machine learning techniques. Although much attention has been paid to large and complex data sets resulting from high-throughput sequencing, advances in high-quality video recording technology have begun to generate similarly rich data sets requiring sophisticated techniques from both computer vision and time-series analysis… Moreover, just as studying gene expression patterns in one organism can reveal general principles that apply to other organisms, the study […]
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Efficient Certification of Spatial Robustness
Recent work has exposed the vulnerability of computer vision models to spatial transformations. Due to the widespread usage of such models in safety-critical applications, it is crucial to quantify their robustness against spatial transformations… However, existing work only provides empirical quantification of spatial robustness via adversarial attacks, which lack provable guarantees. In this work, we propose novel convex relaxations, which enable us, for the first time, to provide a certificate of robustness against spatial transformations. Our convex relaxations are model-agnostic […]
Read moreA Detailed Study of Self Supervised Contrastive Loss and Supervised Contrastive Loss
Introduction Supervised Contrastive Learning paper claims a big deal about supervised learning and cross-entropy loss vs supervised contrastive loss for better image representation and classification tasks. Let’s go in-depth in this paper what is about. Claim actually close to 1% improvement on image net data set¹. Architecture wise, its a very simple network resnet 50 having a 128-dimensional head. If you want you can add a few more layers as well. Architecture and training process from the paper Codeself.encoder = […]
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