Search Results for author:
Found 18 papers, 6 papers with code
$\mathsf{CSMAE~}$:~Cataract Surgical Masked Autoencoder (MAE) based Pre-training
Automated analysis of surgical videos is crucial for improving surgical training, workflow optimization, and postoperative assessment.
LQMFormer: Language-aware Query Mask Transformer for Referring Image Segmentation
To address this issue we propose a Multi-modal Query Feature Fusion technique characterized by two innovative designs: (1) Gaussian enhanced Multi-Modal Fusion a novel visual grounding mechanism that enhances overall representation by extracting rich local visual information and global visual-linguistic relationships and (2) A Dynamic Query Module that produces a diverse set of queries through a scoring network where the network selectively focuses on queries for objects referred to in the language description.
GLSFormer : Gated - Long, Short Sequence Transformer for Step Recognition in Surgical Videos
These results validate the suitability of our proposed approach for automated surgical step recognition.
Towards Device Efficient Conditional Image Generation
We present a novel algorithm to reduce tensor compute required by a conditional image generation autoencoder without sacrificing quality of photo-realistic image generation.
Can No-reference features help in Full-reference image quality estimation?
Recent works in Full-reference IQA research perform pixelwise comparison between deep features corresponding to query and reference images for quality prediction.
ADAM Challenge: Detecting Age-related Macular Degeneration from Fundus Images
The ADAM challenge consisted of four tasks which cover the main aspects of detecting and characterizing AMD from fundus images, including detection of AMD, detection and segmentation of optic disc, localization of fovea, and detection and segmentation of lesions.
Anomaly Detection in Retinal Images using Multi-Scale Deep Feature Sparse Coding
Convolutional Neural Network models have successfully detected retinal illness from optical coherence tomography (OCT) and fundus images.
QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results
In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation.
MSR-Net: Multi-Scale Relighting Network for One-to-One Relighting
Deep image relighting allows photo enhancement by illumination-specific retouching without human effort and so it is getting much interest lately.
Fast and Accurate Quantized Camera Scene Detection on Smartphones, Mobile AI 2021 Challenge: Report
To address this problem, we introduce the first Mobile AI challenge, where the target is to develop quantized deep learning-based camera scene classification solutions that can demonstrate a real-time performance on smartphones and IoT platforms.
Stacked Deep Multi-Scale Hierarchical Network for Fast Bokeh Effect Rendering from a Single Image
Most of the existing methods utilized additional sensor's data or pretrained network for fine depth estimation of the scene and sometimes use portrait segmentation pretrained network module to segment salient objects in the image.
Efficient Space-time Video Super Resolution using Low-Resolution Flow and Mask Upsampling
Input LR frames are super-resolved using a state-of-the-art Video Super-Resolution method.
Colorectal Cancer Segmentation using Atrous Convolution and Residual Enhanced UNet
Colorectal cancer is a leading cause of death worldwide.
DSRN: an Efficient Deep Network for Image Relighting
Custom and natural lighting conditions can be emulated in images of the scene during post-editing.
Ranked #4 on
Image Relighting
on VIDIT’20 validation set
AIM 2020 Challenge on Rendering Realistic Bokeh
This paper reviews the second AIM realistic bokeh effect rendering challenge and provides the description of the proposed solutions and results.
AIM 2020: Scene Relighting and Illumination Estimation Challenge
The first track considered one-to-one relighting; the objective was to relight an input photo of a scene with a different color temperature and illuminant orientation (i. e., light source position).
UDC 2020 Challenge on Image Restoration of Under-Display Camera: Methods and Results
The results in the paper are state-of-the-art restoration performance of Under-Display Camera Restoration.
NTIRE 2020 Challenge on Real Image Denoising: Dataset, Methods and Results
This challenge is based on a newly collected validation and testing image datasets, and hence, named SIDD+.
