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Found 10 papers, 2 papers with code
Uncertainty-guided Open-Set Source-Free Unsupervised Domain Adaptation with Target-private Class Segregation
We propose a novel approach for SF-OSDA that exploits the granularity of target-private categories by segregating their samples into multiple unknown classes.
Synchronization is All You Need: Exocentric-to-Egocentric Transfer for Temporal Action Segmentation with Unlabeled Synchronized Video Pairs
Instead, we propose a novel methodology which performs the adaptation leveraging existing labeled exocentric videos and a new set of unlabeled, synchronized exocentric-egocentric video pairs, for which temporal action segmentation annotations do not need to be collected.
Adapting Vision Foundation Models for Plant Phenotyping
The experimental results show that a foundation model can be efficiently adapted to multiple plant phenotyping tasks, yielding similar performance as the state-of-the-art (SoTA) models specifically designed or trained for each task.
An Omnidirectional Approach to Touch-based Continuous Authentication
This paper focuses on how touch interactions on smartphones can provide a continuous user authentication service through behaviour captured by a touchscreen.
Transfer Learning via Test-Time Neural Networks Aggregation
The proposed aggregation loss allows our model to learn how trained deep network parameters can be aggregated with an aggregation operator.
Blind Inpainting of Large-scale Masks of Thin Structures with Adversarial and Reinforcement Learning
We evaluate our method in several datasets in medical imaging, plant science, and remote sensing.
Leveraging multiple datasets for deep leaf counting
While state-of-the-art results on leaf counting with deep learning methods have recently been reported, they obtain the count as a result of leaf segmentation and thus require per-leaf (instance) segmentation to train the models (a rather strong annotation).
ARIGAN: Synthetic Arabidopsis Plants using Generative Adversarial Network
We show that our model is able to generate realistic 128x128 colour images of plants.
Theta-RBM: Unfactored Gated Restricted Boltzmann Machine for Rotation-Invariant Representations
In this paper, we propose the Theta-Restricted Boltzmann Machine ({\theta}-RBM in short), which builds upon the original RBM formulation and injects the notion of rotation-invariance during the learning procedure.
Rotation-Invariant Restricted Boltzmann Machine Using Shared Gradient Filters
Finding suitable features has been an essential problem in computer vision.
