EurekAlert!

A review on multi-view learning

Review re-maps multi-view learning into four supervised scenarios and three granular sub-tiers, delivering the first unified ...
Frontiers

Semi-supervised segmentation of forest fires from UAV remote sensing images via panoramic feature fusion and pixel contrastive learning

1 School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, China 2 College of Information Science and Technology, Nanjing Forestry University, Nanjing, China ...
Optics

Optimizing deployment costs of inspection functions with Inference Deep Learning Licenses.

Euresys announce the availability of Cost-Effective Inference Licenses for image classification, supervised or unsupervised segmentation and object localization. When implementing Deep Learning on ...
GitHub

microCLIP: Unsupervised CLIP Adaptation via Coarse-Fine Token Fusion for Fine-Grained Image Classification

microCLIP is a lightweight self-training framework that adapts CLIP for fine-grained image classification without requiring labeled data. While CLIP is strong in zero-shot transfer, it primarily ...
IEEE

Unsupervised, Untrained and Robust Single Image Superpixel Segmentation Network

Abstract: Image superpixel segmentation has greatly benefited from the excellent feature extraction capabilities of neural networks. However, most existing neural network-based superpixel segmentation ...
EurekAlert!

Unsupervised cross-domain adaptation: single-pixel image transmission in unknown dynamic channels

Imaging technologies are ubiquitous in our daily lives, from smartphone cameras to medical imaging devices, helping us capture images and perceive objects. However, when faced with complex ...
IEEE

WeakSegNet: Combining Unsupervised, Few-Shot, and Weakly Supervised Methods for the Semantic Segmentation of Low-Magnification Effusion Cytology Images

Abstract: Effusion cytology analysis can be time consuming for cytopathologists, but the burden can be reduced through automatic malignancy detection. The main challenge in the automation process is ...
Frontiers

Training marine species object detectors with synthetic images and unsupervised domain adaptation

1 Australian Centre for Robotics, University of Sydney, Sydney, NSW, Australia 2 Department of Marine Technology, Norwegian University of Science and Technology, Trondheim, Norway Visual surveys by ...
GitHub

Self-Supervised Learning for Crop Classification and Yield Prediction

Leibniz-Centre for Agricultural Landscape Research (ZALF) e.V. Email: stefan [dot] stiller [at] zalf [dot] de, stillsen [at] gmail [dot] com This repository contains the code for the study ...