MaskCon: Masked Contrastive Learning for Coarse-Labeled Dataset
A masked contrastive learning framework for learning meaningful fine-grained representations with coarse-labeled dataset.
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A masked contrastive learning framework for learning meaningful fine-grained representations with coarse-labeled dataset.
A self-supervised learning method aiming to alleviate the inherent false-negative problem in contrastive learning framework.
A robust and efficient training framework tackling with dataset with noisy labels.
A self-supervised pre-training method with focus on alleviating class collision problem using a cross-context learning scheme.
A CLIP-based visual-language model called DFER-CLIP for in-the-wild dynamic facial expression of emotion recognition.
Leverages the association between parts of speech and specific visual modes of variation to better separate representations of style from content in the CLIP representations
Localized image editing through joint factorization of parts of appearances in pre-trained GANs.
A neural network-based time-series forecasting model for concentrations of an electrochemical reaction.
The Aristotle University of Thessaloniki (hereinafter, AUTH) created the dataset ‘3D-Flood’, within the context of the project TEMA that was funded by the European Commission-European Union [Grant Agreement number: 101093003; start date: 01/12/2022; end d...
The VesselAI Data Harmonisation Services is a set of services provided through an User Interface for harmonising Maritime Domain-related data sources with Big Data characteristics. This harmonisation process entails the mapping of raw data schemas/formats...