A5066174369- Multimodal Machine Learning Applications
- Horticultural and Viticultural Research
- 3D Surveying and Cultural Heritage
- Plant Water Relations and Carbon Dynamics
- Plant Physiology and Cultivation Studies
- 3D Shape Modeling and Analysis
- Domain Adaptation and Few-Shot Learning
- Forest ecology and management
- Remote Sensing and LiDAR Applications
- Industrial Vision Systems and Defect Detection
- Neural Networks and Applications
- Advanced Vision and Imaging
- Advanced Image and Video Retrieval Techniques
- COVID-19 diagnosis using AI
- Plant and animal studies
- Anomaly Detection Techniques and Applications
- Image Processing and 3D Reconstruction
The Scarborough Hospital
2024-2025
University of Toronto
2024-2025
University of Technology Sydney
2020-2024
Niagara College
2024
Baidu (China)
2023
In this paper, we investigate Universal Domain Adaptation (UniDA) problem, which aims to transfer the knowledge from source target under unaligned label space. The main challenge of UniDA lies in how separate common classes (i.e., shared across domains), private only exist one domain). Previous works treat samples as generic class but ignore their intrinsic structure. Consequently, resulting representations are not compact enough latent space and can be easily confused with samples. To...
In this paper, we present a new dataset with the target of advancing scene parsing task from images to videos. Our aims perform Video Scene Parsing in Wild (VSPW), which covers wide range real-world scenarios and categories. To be specific, our VSPW is featured following aspects: 1) Well-trimmed long-temporal clips. Each video contains complete shot, lasting around 5 seconds on average. 2) Dense annotation. The pixel-level annotations are provided at high frame rate 15 f/s. 3) High...
This paper considers the synthetic-to-real adaptation of point cloud semantic segmentation, which aims to segment real-world clouds with only synthetic labels available. Contrary data is integral and clean, collected by sensors typically contain unexpected irregular noise because may be impacted various environmental conditions. Consequently, model trained on ideal fail achieve satisfactory segmentation results real data. Influenced such noise, previous adversarial training methods, are...
Abstract Quantifying drought tolerance in crops is critical for agriculture management under environmental change, and response traits grape vine have long been the focus of viticultural research. Turgor loss point ( π tlp ) gaining attention as an indicator plants, though estimating often requires construction analysis pressure-volume (P-V) curves which are very time consuming. While P-V remain a valuable tool assessing related traits, there considerable interest developing high-throughput...
<title>Abstract</title> Quantifying drought tolerance in crops is critical for agricultural management under environmental change, and response traits wine grapes have long been the focus of viticultural research. Turgor loss point (<italic>π</italic><sub>tlp</sub>) gaining attention as an indicator plants, though estimating <italic>π</italic><sub>tlp</sub> often requires construction analysis pressure-volume (P-V) curves which time consuming. While P-V remain a valuable tool assessing...