no code implementations • 23 Apr 2022 • Rajeev Yasarla, Vishwanath A. Sindagi, Vishal M. Patel
Existing approaches for restoring weather-degraded images follow a fully-supervised paradigm and they require paired data for training.
no code implementations • 27 May 2021 • Poojan Oza, Vishwanath A. Sindagi, Vibashan VS, Vishal M. Patel
Recent advances in deep learning have led to the development of accurate and efficient models for various computer vision applications such as classification, segmentation, and detection.
no code implementations • CVPR 2021 • Vibashan VS, Vikram Gupta, Poojan Oza, Vishwanath A. Sindagi, Vishal M. Patel
Existing approaches for unsupervised domain adaptive object detection perform feature alignment via adversarial training.
1 code implementation • 4 Oct 2020 • Jeya Maria Jose Valanarasu, Vishwanath A. Sindagi, Ilker Hacihaliloglu, Vishal M. Patel
To overcome this issue, we propose using an overcomplete convolutional architecture where we project our input image into a higher dimension such that we constrain the receptive field from increasing in the deep layers of the network.
Ranked #1 on Medical Image Segmentation on RITE
1 code implementation • 14 Sep 2020 • Deepak Babu Sam, Abhinav Agarwalla, Jimmy Joseph, Vishwanath A. Sindagi, R. Venkatesh Babu, Vishal M. Patel
Dense crowd counting is a challenging task that demands millions of head annotations for training models.
no code implementations • ECCV 2020 • Vishwanath A. Sindagi, Rajeev Yasarla, Deepak Sam Babu, R. Venkatesh Babu, Vishal M. Patel
In this work, we focus on reducing the annotation efforts by learning to count in the crowd from limited number of labeled samples while leveraging a large pool of unlabeled data.
no code implementations • 7 Apr 2020 • Vishwanath A. Sindagi, Rajeev Yasarla, Vishal M. Patel
The proposed Confidence Guided Deep Residual Counting Network (CG-DRCN) is evaluated on recent complex datasets, and it achieves significant improvements in errors.
no code implementations • ECCV 2020 • Vishwanath A. Sindagi, Poojan Oza, Rajeev Yasarla, Vishal M. Patel
Adverse weather conditions such as haze and rain corrupt the quality of captured images, which cause detection networks trained on clean images to perform poorly on these images.
no code implementations • ICCV 2019 • Vishwanath A. Sindagi, Rajeev Yasarla, Vishal M. Patel
The proposed Confidence Guided Deep Residual Counting Network (CG-DRCN) is evaluated on recent complex datasets, and it achieves significant improvements in errors.
no code implementations • ICCV 2019 • Vishwanath A. Sindagi, Vishal M. Patel
These issues are further exacerbated in highly congested scenes.
no code implementations • 24 Jul 2019 • Vishwanath A. Sindagi, Vishal M. Patel
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).
no code implementations • 2 Jul 2019 • Vishwanath A. Sindagi, Vishal M. Patel
In this paper, we address the challenging problem of crowd counting in congested scenes.
1 code implementation • 2 Apr 2019 • Vishwanath A. Sindagi, Yin Zhou, Oncel Tuzel
Many recent works on 3D object detection have focused on designing neural network architectures that can consume point cloud data.
Ranked #7 on 3D Object Detection on DAIR-V2X-I
no code implementations • 16 Jan 2019 • Vishwanath A. Sindagi, Vishal M. Patel
In this work, we approach the problem of small face detection with the motivation of enriching the feature maps using a density map estimation module.
no code implementations • 26 Apr 2018 • Hajime Nada, Vishwanath A. Sindagi, He Zhang, Vishal M. Patel
In this work, we identify the next set of challenges that requires attention from the research community and collect a new dataset of face images that involve these issues such as weather-based degradations, motion blur, focus blur and several others.
1 code implementation • 27 Oct 2017 • Lidan Wang, Vishwanath A. Sindagi, Vishal M. Patel
To this end, we propose a novel synthesis framework called Photo-Sketch Synthesis using Multi-Adversarial Networks, (PS2-MAN) that iteratively generates low resolution to high resolution images in an adversarial way.
Ranked #2 on Face Sketch Synthesis on CUHK
2 code implementations • 3 Oct 2017 • Xing Di, Vishwanath A. Sindagi, Vishal M. Patel
The primary aim of this work is to demonstrate that information preserved by landmarks (gender in particular) can be further accentuated by leveraging generative models to synthesize corresponding faces.
no code implementations • ICCV 2017 • Vishwanath A. Sindagi, Vishal M. Patel
DME is a multi-column architecture-based CNN that aims to generate high-dimensional feature maps from the input image which are fused with the contextual information estimated by GCE and LCE using F-CNN.
Ranked #8 on Crowd Counting on WorldExpo’10
1 code implementation • 30 Jul 2017 • Vishwanath A. Sindagi, Vishal M. Patel
Estimating crowd count in densely crowded scenes is an extremely challenging task due to non-uniform scale variations.
Ranked #16 on Crowd Counting on UCF-QNRF
1 code implementation • 5 Jul 2017 • Vishwanath A. Sindagi, Vishal M. Patel
Nevertheless, over the last few years, crowd count analysis has evolved from earlier methods that are often limited to small variations in crowd density and scales to the current state-of-the-art methods that have developed the ability to perform successfully on a wide range of scenarios.