Search Results for author:
Found 14 papers, 6 papers with code
Unsupervised Collaborative Metric Learning with Mixed-Scale Groups for General Object Retrieval
This paper presents a novel unsupervised deep metric learning approach, termed unsupervised collaborative metric learning with mixed-scale groups (MS-UGCML), devised to learn embeddings for objects of varying scales.
Coded Residual Transform for Generalizable Deep Metric Learning
To address this challenge, in this paper, we introduce a new method called coded residual transform (CRT) for deep metric learning to significantly improve its generalization capability.
Exploring Contextual Relationships for Cervical Abnormal Cell Detection
To mimic these behaviors, we propose to explore contextual relationships to boost the performance of cervical abnormal cell detection.
M2MRF: Many-to-Many Reassembly of Features for Tiny Lesion Segmentation in Fundus Images
It reassembles features in a dimension-reduced feature space and simultaneously aggregates multiple features inside a large predefined region into multiple target features.
Dual-Branch Network with Dual-Sampling Modulated Dice Loss for Hard Exudate Segmentation from Colour Fundus Images
In detail, for the first branch, we use a uniform sampler to sample pixels from predicted segmentation mask for Dice loss calculation, which leads to this branch naturally be biased in favour of large hard exudates as Dice loss generates larger cost on misidentification of large hard exudates than small hard exudates.
A Novel Automation-Assisted Cervical Cancer Reading Method Based on Convolutional Neural Network
Our model achieves 97. 5% sensitivity (Sens) and 67. 8% specificity (Spec) on cervical cell image-level screening.
A Deep Gradient Boosting Network for Optic Disc and Cup Segmentation
To build connections among prediction branches, this paper introduces gradient boosting framework to deep classification model and proposes a gradient boosting network called BoostNet.
Dual-attention Focused Module for Weakly Supervised Object Localization
In each branch, the input feature map is deduced into an enhancement map and a mask map, thereby highlighting the most discriminative parts or hiding them.
Efficient Misalignment-Robust Multi-Focus Microscopical Images Fusion
Our method provides a flexible and efficient way to integrate complementary and redundant information from multiple multi-focus ultra HD unregistered images into a fused image that contains better description than any of the individual input images.
Scale-Invariant Structure Saliency Selection for Fast Image Fusion
Specifically, we propose a scale-invariant structure saliency selection scheme based on the difference-of-Gaussian (DoG) pyramid of images to build the weights or activity map.
Comparison-Based Convolutional Neural Networks for Cervical Cell/Clumps Detection in the Limited Data Scenario
In this paper we propose an efficient CNN-based object detection methods for cervical cancer cells/clumps detection.
CNN-Based Automatic Urinary Particles Recognition
The urine sediment analysis of particles in microscopic images can assist physicians in evaluating patients with renal and urinary tract diseases.
Feature selection via simultaneous sparse approximation for person specific face verification
There is an increasing use of some imperceivable and redundant local features for face recognition.
Feature Selection via Sparse Approximation for Face Recognition
Inspired by biological vision systems, the over-complete local features with huge cardinality are increasingly used for face recognition during the last decades.
