Search Results for author:
Found 15 papers, 6 papers with code
CUPre: Cross-domain Unsupervised Pre-training for Few-Shot Cell Segmentation
While pre-training on object detection tasks, such as Common Objects in Contexts (COCO) [1], could significantly boost the performance of cell segmentation, it still consumes on massive fine-annotated cell images [2] with bounding boxes, masks, and cell types for every cell in every image, to fine-tune the pre-trained model.
Advancements in Repetitive Action Counting: Joint-Based PoseRAC Model With Improved Performance
Repetitive counting (RepCount) is critical in various applications, such as fitness tracking and rehabilitation.
Attention-Enhanced Co-Interactive Fusion Network (AECIF-Net) for Automated Structural Condition Assessment in Visual Inspection
Efficiently monitoring the condition of civil infrastructure requires automating the structural condition assessment in visual inspection.
Semi-supervised Domain Adaptive Medical Image Segmentation through Consistency Regularized Disentangled Contrastive Learning
In this work, we investigate relatively less explored semi-supervised domain adaptation (SSDA) for medical image segmentation, where access to a few labeled target samples can improve the adaptation performance substantially.
Pseudo-Label Guided Contrastive Learning for Semi-Supervised Medical Image Segmentation
Although recent works in semi-supervised learning (SemiSL) have accomplished significant success in natural image segmentation, the task of learning discriminative representations from limited annotations has been an open problem in medical images.
An Attention-guided Multistream Feature Fusion Network for Localization of Risky Objects in Driving Videos
To this end, this paper proposes an attention-guided multistream feature fusion network (AM-Net) to localize dangerous traffic agents from dashcam videos.
A Deep Neural Network for Multiclass Bridge Element Parsing in Inspection Image Analysis
Aerial robots such as drones have been leveraged to perform bridge inspections.
A semi-supervised self-training method to develop assistive intelligence for segmenting multiclass bridge elements from inspection videos
This paper is motivated to develop an assistive intelligence model for segmenting multiclass bridge elements from inspection videos captured by an aerial inspection platform.
A Dynamic Spatial-temporal Attention Network for Early Anticipation of Traffic Accidents
Visual cues for predicting a future accident are embedded deeply in dashcam video data.
Ranked #1 on
Accident Anticipation
on CCD
A system of vision sensor based deep neural networks for complex driving scene analysis in support of crash risk assessment and prevention
The paper further evaluates the performance of the Multi-Net and the efficiency of the developed system.
Vision-based Price Suggestion for Online Second-hand Items
Then, we design a vision-based price suggestion module which takes the extracted visual features along with some statistical item features from the shopping platform as the inputs to determine whether an uploaded item image is qualified for price suggestion by a binary classification model, and provide price suggestions for items with qualified images by a regression model.
Price Suggestion for Online Second-hand Items with Texts and Images
The goal of price prediction is to help sellers set effective and reasonable prices for their second-hand items with the images and text descriptions uploaded to the online platforms.
3D Graph Anatomy Geometry-Integrated Network for Pancreatic Mass Segmentation, Diagnosis, and Quantitative Patient Management
We exploit the feasibility to distinguish pancreatic ductal adenocarcinoma (PDAC) from the nine other nonPDAC masses using multi-phase CT imaging.
Multi-Modal Recognition of Worker Activity for Human-Centered Intelligent Manufacturing
In a human-centered intelligent manufacturing system, sensing and understanding of the worker's activity are the primary tasks.
Active Sample Selection and Correction Propagation on a Gradually-Augmented Graph
When data have a complex manifold structure or the characteristics of data evolve over time, it is unrealistic to expect a graph-based semi-supervised learning method to achieve flawless classification given a small number of initial annotations.
