In this paper, we consider an interesting vision problem—salient instance segmentation. Other than producing approximate bounding boxes, our network also outputs high-quality instance-level segments. Taking into account the category-independent property of each target, we design a single stage salient instance segmentation framework, with a novel segmentation branch. Our new branch regards not ...

We show that an adaptive robust filtering based image segmentation model can be used for microvasculature network detection and quantitative characterization in epifluorescence-based high resolution images. Inhomogeneous fluorescence contrast due to the variable binding properties of the lectin marker and leakage hampers accurate vessel segmentation. We use a robust adaptive filtering approach ...

The problem of finding a Bayesian network structure which maximizes a score function is known as Bayesian network structure learning from data. We study this problem in this paper with respect to a decomposable score function. Solving this problem is known to be NP-hard. Several algorithms are proposed to overcome this problem such as hill-climbing, dynamic programming, branch and bound, and so...

In the field of building detection research, an accurate, state-of-the-art semantic segmentation model must be constructed to classify each pixel image, which has important reference value for statistical work a area. Recent research efforts have been devoted using deep learning approaches, can further divided into two aspects. this paper, we propose single-side dual-branch network (SSDBN) base...

Microarchitectural prediction based on machine learning has received increasing attention in recent years. The common problem is that most of the Neural Network Based Branch Predictors (NNBBP) can achieve high accuracy; however, that achievement is not big enough to offset the cost of latency. As a result, the idea of using Neural Networks (NN) to perform branch prediction is not practically us...