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Unsupervised Domain Adaptation of Object Detectors: A Survey
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. However, learning highly accurate models relies on the availability of large-scale annotated datasets. Due to this, model performance drops drastically when evaluated on label-scarce datasets having visually dist...Show More
Source-free Unsupervised Domain Adaptation for 3D Object Detection in Adverse Weather
Deepti Hegde;Velat Kilic;Vishwanath Sindagi;A Brinton Cooper;Mark Foster;Vishal M Patel
2023 IEEE International Conference on Robotics and Automation (ICRA)
Year: 2023 | Conference Paper |
Cited by: Papers (8)
A domain shift exists between the distributions of large scale, outdoor lidar datasets due to being captured using different types of lidar sensors, in different locations, and under varying weather conditions. Inclement weather in particular affects the quality of lidar data, adding artifacts such as scattered and missed points, leading to a drop in performance of 3D object detection networks tra...Show More
Unsupervised Restoration of Weather-affected Images using Deep Gaussian Process-based CycleGAN
2022 26th International Conference on Pattern Recognition (ICPR)
Year: 2022 | Conference Paper |
Existing approaches for restoring weather-degraded images follow a fully-supervised paradigm and they require paired data for training. However, collecting paired data for weather degradations is extremely challenging, and existing methods end up training on synthetic data. To overcome this issue, we describe an approach for supervising deep networks that is based on CycleGAN, thereby enabling the...Show More
Mixture of Teacher Experts for Source-Free Domain Adaptive Object Detection
Vibashan Vs;Poojan Oza;Vishwanath A. Sindagi;Vishal M. Patel
2022 IEEE International Conference on Image Processing (ICIP)
Year: 2022 | Conference Paper |
Cited by: Papers (4)
Unsupervised domain adaptive object detection methods transfer knowledge from the labelled source domain to a visually distinct and unlabeled target domain. Most methods achieve this by training the detector model with the help of both labeled source and unlabeled target data. However, in real-world scenarios, gaining access to source data is not practical due to privacy concerns, legal issues and...Show More
KiU-Net: Overcomplete Convolutional Architectures for Biomedical Image and Volumetric Segmentation
Jeya Maria Jose Valanarasu;Vishwanath A. Sindagi;Ilker Hacihaliloglu;Vishal M. Patel
Most methods for medical image segmentation use U-Net or its variants as they have been successful in most of the applications. After a detailed analysis of these “traditional” encoder-decoder based approaches, we observed that they perform poorly in detecting smaller structures and are unable to segment boundary regions precisely. This issue can be attributed to the increase in receptive field si...Show More
JHU-CROWD++: Large-Scale Crowd Counting Dataset and A Benchmark Method
We introduce a new large scale unconstrained crowd counting dataset (JHU-CROWD++) that contains “4,372” images with “1.51 million” annotations. In comparison to existing datasets, the proposed dataset is collected under a variety of diverse scenarios and environmental conditions. Specifically, the dataset includes several images with weather-based degradations and illumination variations, making i...Show More
MeGA-CDA: Memory Guided Attention for Category-Aware Unsupervised Domain Adaptive Object Detection
Vibashan VS;Vikram Gupta;Poojan Oza;Vishwanath A. Sindagi;Vishal M. Patel
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2021 | Conference Paper |
Cited by: Papers (111)
Existing approaches for unsupervised domain adaptive object detection perform feature alignment via adversarial training. While these methods achieve reasonable improvements in performance, they typically perform category-agnostic domain alignment, thereby resulting in negative transfer of features. To overcome this issue, in this work, we attempt to incorporate category information into the domai...Show More
Semi-Supervised Image Deraining Using Gaussian Processes
Recent CNN-based methods for image deraining have achieved excellent performance in terms of reconstruction error as well as visual quality. However, these methods are limited in the sense that they can be trained only on fully labeled data. Due to various challenges in obtaining real world fully-labeled image deraining datasets, existing methods are trained only on synthetically generated data an...Show More
Syn2Real Transfer Learning for Image Deraining Using Gaussian Processes
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2020 | Conference Paper |
Cited by: Papers (146)
Recent CNN-based methods for image deraining have achieved excellent performance in terms of reconstruction error as well as visual quality. However, these methods are limited in the sense that they can be trained only on fully labeled data. Due to various challenges in obtaining real world fully-labeled image deraining datasets, existing methods are trained only on synthetically generated data an...Show More
HA-CCN: Hierarchical Attention-Based Crowd Counting Network
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 the 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 VG...Show More
Image De-Raining Using a Conditional Generative Adversarial Network
Severe weather conditions, such as rain and snow, adversely affect the visual quality of images captured under such conditions, thus rendering them useless for further usage and sharing. In addition, such degraded images drastically affect the performance of vision systems. Hence, it is important to address the problem of single image de-raining. However, the inherent ill-posed nature of the probl...Show More
Joint Transmission Map Estimation and Dehazing Using Deep Networks
Single image haze removal is an extremely challenging problem due to its inherent ill-posed nature. Several prior-based and learning-based methods have been proposed in the literature to solve this problem and they have achieved visually appealing results. However, most of the existing methods assume constant atmospheric light model and tend to follow a two-step procedure involving prior-based met...Show More
Pushing the Frontiers of Unconstrained Crowd Counting: New Dataset and Benchmark Method
2019 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2019 | Conference Paper |
Cited by: Papers (56)
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 gui...Show More
Multi-Level Bottom-Top and Top-Bottom Feature Fusion for Crowd Counting
2019 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2019 | Conference Paper |
Cited by: Papers (138)
Crowd counting presents enormous challenges in the form of large variation in scales within images and across the dataset. These issues are further exacerbated in highly congested scenes. Approaches based on straightforward fusion of multi-scale features from a deep network seem to be obvious solutions to this problem. However, these fusion approaches do not yield significant improvements in the c...Show More
Inverse Attention Guided Deep Crowd Counting Network
2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)
Year: 2019 | Conference Paper |
Cited by: Papers (24)
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 trai...Show More
MVX-Net: Multimodal VoxelNet for 3D Object Detection
2019 International Conference on Robotics and Automation (ICRA)
Year: 2019 | Conference Paper |
Cited by: Papers (274)
Many recent works on 3D object detection have focused on designing neural network architectures that can consume point cloud data. While these approaches demonstrate encouraging performance, they are typically based on a single modality and are unable to leverage information from other modalities, such as a camera. Although a few approaches fuse data from different modalities, these methods either...Show More
DAFE-FD: Density Aware Feature Enrichment for Face Detection
2019 IEEE Winter Conference on Applications of Computer Vision (WACV)
Year: 2019 | Conference Paper |
Cited by: Papers (14)
Recent research on face detection, which is focused primarily on improving accuracy of detecting smaller faces, attempt to develop new anchor design strategies to facilitate increased overlap between anchor boxes and ground truth faces of smaller sizes. In this work, we approach the problem of small face detection with the motivation of enriching the feature maps using a density map estimation mod...Show More
Pushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline Results
2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS)
Year: 2018 | Conference Paper |
Cited by: Papers (51)
Face detection has witnessed immense progress in the last few years, with new milestones being surpassed every year. While many challenges such as large variations in scale, pose, appearance are successfully addressed, there still exist several issues which are not specifically captured by existing methods or datasets. In this work, we identify the next set of challenges that requires attention fr...Show More
NTIRE 2018 Challenge on Image Dehazing: Methods and Results
Cosmin Ancuti;Codruta O. Ancuti;Radu Timofte;Luc Van Gool;Lei Zhang;Ming-Hsuan Yang;Vishal M. Patel;He Zhang;Vishwanath A. Sindagi;Ruhao Zhao;Xiaoping Ma;Yong Qin;Limin Jia;Klaus Friedel;Sehwan Ki;Hyeonjun Sim;Jae-Seok Choi;Sooye Kim;Soomin Seo;Saehun Kim;Munchurl Kim;Ranjan Mondal;Sanchayan Santra;Bhabatosh Chanda;Jinlin Liu;Kangfu Mei;Juncheng Li;Luyao;Faming Fang;Aiwen Jiang;Xiaochao Qu;Ting Liu;Pengfei Wang;Biao Sun;Jiangfan Deng;Yuhang Zhao;Ming Hong;Jingying Huang;Yizhi Chen;Erin Chen;Xiaoli Yu;Tingting Wu;Anil Genc;Deniz Engin;Hazim Kemal Ekenel;Wenzhe Liu;Tong Tong;Gen Li;Qinquan Gao;Zhan Li;Daofa Tang;Yuling Chen;Ziying Huo;Aitor Alvarez-Gila;Adrian Galdran;Alessandro Bria;Javier Vazquez-Corral;Marcelo Bertalmo;H. Seckin Demir;Omer Faruk Adil;Huynh Xuan Phung;Xin Jin;Jiale Chen;Chaowei Shan;Zhibo Chen
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2018 | Conference Paper |
Cited by: Papers (87)
This paper reviews the first challenge on image dehazing (restoration of rich details in hazy image) with focus on proposed solutions and results. The challenge had 2 tracks. Track 1 employed the indoor images (using I-HAZE dataset), while Track 2 outdoor images (using O-HAZE dataset). The hazy images have been captured in presence of real haze, generated by professional haze machines. I-HAZE data...Show More
NTIRE 2018 Challenge on Image Dehazing: Methods and Results
Cosmin Ancuti;Codruta O. Ancuti;Radu Timofte;Luc Van Gool;Lei Zhang;Ming-Hsuan Yang;Vishal M. Patel;He Zhang;Vishwanath A. Sindagi;Ruhao Zhao;Xiaoping Ma;Yong Qin;Limin Jia;Klaus Friedel;Sehwan Ki;Hyeonjun Sim;Jae-Seok Choi;Sooye Kim;Soomin Seo;Saehun Kim;Munchurl Kim;Ranjan Mondal;Sanchayan Santra;Bhabatosh Chanda;Jinlin Liu;Kangfu Mei;Juncheng Li;Luyao;Faming Fang;Aiwen Jiang;Xiaochao Qu;Ting Liu;Pengfei Wang;Biao Sun;Jiangfan Deng;Yuhang Zhao;Ming Hong;Jingying Huang;Yizhi Chen;Erin Chen;Xiaoli Yu;Tingting Wu;Anil Genc;Deniz Engin;Hazim Kemal Ekenel;Wenzhe Liu;Tong Tong;Gen Li;Qinquan Gao;Zhan Li;Daofa Tang;Yuling Chen;Ziying Huo;Aitor Alvarez-Gila;Adrian Galdran;Alessandro Bria;Javier Vazquez-Corral;Marcelo Bertalmo;H. Seckin Demir;Omer Faruk Adil;Huynh Xuan Phung;Xin Jin;Jiale Chen;Chaowei Shan;Zhibo Chen
Year: 2018 | Conference Paper |
Multi-scale Single Image Dehazing Using Perceptual Pyramid Deep Network
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2018 | Conference Paper |
Cited by: Papers (123)
Haze adversely degrades quality of an image thereby affecting its aesthetic appeal and visibility in outdoor scenes. Single image dehazing is particularly challenging due to its ill-posed nature. Most existing work, including the recent convolutional neural network (CNN) based methods, rely on the classical mathematical formulation where the hazy image is modeled as the superposition of attenuated...Show More
GP-GAN: Gender Preserving GAN for Synthesizing Faces from Landmarks
2018 24th International Conference on Pattern Recognition (ICPR)
Year: 2018 | Conference Paper |
Facial landmarks constitute the most compressed representation of faces and are known to preserve information such as pose, gender and facial structure present in the faces. Several works exist that attempt to perform high-level face-related analysis tasks based on landmarks alone without the aid of face images. In contrast, in this work, an attempt is made to tackle the inverse problem of synthes...Show More
High-Quality Facial Photo-Sketch Synthesis Using Multi-Adversarial Networks
2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018)
Year: 2018 | Conference Paper |
Cited by: Papers (104)
Synthesizing face sketches from real photos and its inverse have many applications. However, photo/sketch synthesis remains a challenging problem due to the fact that photo and sketch have different characteristics. In this work, we consider this task as an image-to-image translation problem and explore the recently popular generative models (GANs) to generate high-quality realistic photos from sk...Show More
Generating High-Quality Crowd Density Maps Using Contextual Pyramid CNNs
2017 IEEE International Conference on Computer Vision (ICCV)
Year: 2017 | Conference Paper |
Cited by: Papers (478)
We present a novel method called Contextual Pyramid CNN (CP-CNN) for generating high-quality crowd density and count estimation by explicitly incorporating global and local contextual information of crowd images. The proposed CP-CNN consists of four modules: Global Context Estimator (GCE), Local Context Estimator (LCE), Density Map Estimator (DME) and a Fusion-CNN (F-CNN). GCE is a VGG-16 based CN...Show More
CNN-Based cascaded multi-task learning of high-level prior and density estimation for crowd counting
2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)
Year: 2017 | Conference Paper |
Cited by: Papers (350)
Estimating crowd count in densely crowded scenes is an extremely challenging task due to non-uniform scale variations. In this paper, we propose a novel end-to-end cascaded network of CNNs to jointly learn crowd count classification and density map estimation. Classifying crowd count into various groups is tantamount to coarsely estimating the total count in the image thereby incorporating a high-...Show More
OLED panel defect detection using local inlier-outlier ratios and modified LBP
2015 14th IAPR International Conference on Machine Vision Applications (MVA)
Year: 2015 | Conference Paper |
We present an automated system for detecting surface defects on OLED panels. These panels exhibit varying textures and patterns which complicates the defect detection process. These detection systems have to be highly accurate and reliable as even a small error in detection can cause huge losses. In this paper, we present a method for detection of OLED panel surface defects using a novel and simpl...Show More
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