Search Results for author:
Found 20 papers, 7 papers with code
Unsupervised Restoration of Weather-affected Images using Deep Gaussian Process-based CycleGAN
Existing approaches for restoring weather-degraded images follow a fully-supervised paradigm and they require paired data for training.
Unsupervised Domain Adaptation of Object Detectors: A Survey
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.
MeGA-CDA: Memory Guided Attention for Category-Aware Unsupervised Domain Adaptive Object Detection
Existing approaches for unsupervised domain adaptive object detection perform feature alignment via adversarial training.
KiU-Net: Overcomplete Convolutional Architectures for Biomedical Image and Volumetric Segmentation
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
Completely Self-Supervised Crowd Counting via Distribution Matching
Dense crowd counting is a challenging task that demands millions of head annotations for training models.
Learning to Count in the Crowd from Limited Labeled Data
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.
JHU-CROWD++: Large-Scale Crowd Counting Dataset and A Benchmark Method
The proposed Confidence Guided Deep Residual Counting Network (CG-DRCN) is evaluated on recent complex datasets, and it achieves significant improvements in errors.
Prior-based Domain Adaptive Object Detection for Hazy and Rainy Conditions
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.
Pushing the Frontiers of Unconstrained Crowd Counting: New Dataset and Benchmark Method
The proposed Confidence Guided Deep Residual Counting Network (CG-DRCN) is evaluated on recent complex datasets, and it achieves significant improvements in errors.
Multi-Level Bottom-Top and Top-Bottom Feature Fusion for Crowd Counting
These issues are further exacerbated in highly congested scenes.
HA-CCN: Hierarchical Attention-based Crowd Counting 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).
Inverse Attention Guided Deep Crowd Counting Network
In this paper, we address the challenging problem of crowd counting in congested scenes.
MVX-Net: Multimodal VoxelNet for 3D Object Detection
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
DAFE-FD: Density Aware Feature Enrichment for Face Detection
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.
Pushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline Results
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.
High-Quality Facial Photo-Sketch Synthesis Using Multi-Adversarial Networks
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
GP-GAN: Gender Preserving GAN for Synthesizing Faces from Landmarks
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.
Generating High-Quality Crowd Density Maps using Contextual Pyramid CNNs
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
CNN-based Cascaded Multi-task Learning of High-level Prior and Density Estimation for Crowd Counting
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
A Survey of Recent Advances in CNN-based Single Image Crowd Counting and Density Estimation
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.
