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Vishal M. Patel
Johns Hopkins University
220Publications
38H-index
5,596Citations
Publications 224
Newest
Automatic segmentation of anatomical landmarks from ultrasound (US) plays an important role in the management of preterm neonates with a very low birth weight due to the increased risk of developing intraventricular hemorrhage (IVH) or other complications. One major problem in developing an automatic segmentation method for this task is the limited availability of annotated data. To tackle this issue, we propose a novel image synthesis method using multi-scale self attention generator to synthes...
Automatic synthesis of faces from visual attributes is an important problem in computer vision and has wide applications in law enforcement and entertainment. With the advent of deep generative convolutional neural networks (CNNs), attempts have been made to synthesize face images from attributes and text descriptions. In this paper, we take a different approach, where we formulate the original problem as a stage-wise learning problem. We first synthesize the facial sketch corresponding to the v...
Fast and accurate MRI image reconstruction from undersampled data is critically important in clinical practice. Compressed sensing based methods are widely used in image reconstruction but the speed is slow due to the iterative algorithms. Deep learning based methods have shown promising advances in recent years. However, recovering the fine details from highly undersampled data is still challenging. In this paper, we introduce a novel deep learning-based method, Pyramid Convolutional RNN (PC-RN...
1 Citations
Adverse weather conditions such as rain and haze corrupt the quality of captured images, which cause detection networks trained on clean images to perform poorly on these images. To address this issue, we propose an unsupervised prior-based domain adversarial object detection framework for adapting the detectors to different weather conditions. We make the observations that corruptions due to different weather conditions (i) follow the principles of physics and hence, can be mathematically model...
#1Vishwanath A. Sindagi (Johns Hopkins University)H-Index: 10
#2Rajeev Yasarla (Johns Hopkins University)H-Index: 1
Last.Vishal M. Patel (Johns Hopkins University)H-Index: 38
view all 3 authors...
In this work, we propose a novel crowd counting network that progressively generates crowd density maps via residual error estimation. The proposed method uses VGG16 as the backbone network and employs density map generated by the final layer as a coarse prediction to refine and generate finer density maps in a progressive fashion using residual learning. Additionally, the residual learning is guided by an uncertainty-based confidence weighting mechanism that permits the flow of only high-confid...
1 Citations
In this paper, we address the challenging problem of crowd counting in congested scenes. Specifically, we present Inverse Attention Guided Deep Crowd Counting Network (IA-DCCN) that efficiently infuses segmentation information through an inverse attention mechanism into the counting network, resulting in significant improvements. The proposed method, which is based on VGG-16, is a single-step training framework and is simple to implement. The use of segmentation information does not require addi...
5 CitationsSource
Single image-based crowd counting has recently witnessed increased focus, but many leading methods are far from optimal, especially in highly congested scenes. In this paper, we present Hierarchical Attention-based Crowd Counting Network (HA-CCN) that employs attention mechanisms at various levels to selectively enhance the features of the network. The proposed method, which is based on the VGG16 network, consists of a spatial attention module (SAM) and a set of global attention modules (GAM). S...
2 Citations
#1Vishwanath A. Sindagi (Johns Hopkins University)H-Index: 10
#2Vishal M. Patel (Johns Hopkins University)H-Index: 38
In this paper, we address the challenging problem of crowd counting in congested scenes. Specifically, we present Inverse Attention Guided Deep Crowd Counting Network (IA-DCCN) that efficiently infuses segmentation information through an inverse attention mechanism into the counting network, resulting in significant improvements. The proposed method, which is based on VGG-16, is a single-step training framework and is simple to implement. The use of segmentation information results in minimal co...
4 Citations
#1He Zhang (RU: Rutgers University)H-Index: 12
#2Benjamin S. Riggan (ARL: United States Army Research Laboratory)H-Index: 6
Last.Vishal M. Patel (Johns Hopkins University)H-Index: 38
view all 5 authors...
The large domain discrepancy between faces captured in polarimetric (or conventional) thermal and visible domains makes cross-domain face verification a highly challenging problem for human examiners as well as computer vision algorithms. Previous approaches utilize either a two-step procedure (visible feature estimation and visible image reconstruction) or an input-level fusion technique, where different Stokes images are concatenated and used as a multi-channel input to synthesize the visible ...
4 CitationsSource
Jun 1, 2019 in CVPR (Computer Vision and Pattern Recognition)
#1Rajeev Yasarla (Johns Hopkins University)H-Index: 1
#2Vishal M. Patel (Johns Hopkins University)H-Index: 38
4 Citations
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