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pro vyhledávání: '"Scheirer A"'
Autor:
Abraham, Sophia J., Huang, Jin, RichardWebster, Brandon, Milford, Michael, Hauenstein, Jonathan D., Scheirer, Walter
We introduce a unique semantic segmentation dataset of 6,096 high-resolution aerial images capturing indigenous and invasive grass species in Bega Valley, New South Wales, Australia, designed to address the underrepresented domain of ecological data
Externí odkaz:
http://arxiv.org/abs/2408.06356
We combine concept-based neural networks with generative, flow-based classifiers into a novel, intrinsically explainable, exactly invertible approach to supervised learning. Prototypical neural networks, a type of concept-based neural network, repres
Externí odkaz:
http://arxiv.org/abs/2407.12200
Autor:
Theisen, William, Scheirer, Walter
The social media landscape of conflict dynamics has grown increasingly multi-modal. Recent advancements in model architectures such as CLIP have enabled researchers to begin studying the interplay between the modalities of text and images in a shared
Externí odkaz:
http://arxiv.org/abs/2403.12747
Autor:
Theisen, William, Yankoski, Michael, Hook, Kristina, Verdeja, Ernesto, Scheirer, Walter, Weninger, Tim
Governments use propaganda, including through visual content -- or Politically Salient Image Patterns (PSIP) -- on social media, to influence and manipulate public opinion. In the present work, we collected Telegram post-history of from 989 Russian m
Externí odkaz:
http://arxiv.org/abs/2402.14947
Autor:
Pirinen, Aleksis, Abid, Nosheen, Paszkowsky, Nuria Agues, Timoudas, Thomas Ohlson, Scheirer, Ronald, Ceccobello, Chiara, Kovács, György, Persson, Anders
Cloud formations often obscure optical satellite-based monitoring of the Earth's surface, thus limiting Earth observation (EO) activities such as land cover mapping, ocean color analysis, and cropland monitoring. The integration of machine learning (
Externí odkaz:
http://arxiv.org/abs/2311.14024
Surging interest in deep learning from high-stakes domains has precipitated concern over the inscrutable nature of black box neural networks. Explainable AI (XAI) research has led to an abundance of explanation algorithms for these black boxes. Such
Externí odkaz:
http://arxiv.org/abs/2310.18496
Prototypical part neural networks (ProtoPartNNs), namely PROTOPNET and its derivatives, are an intrinsically interpretable approach to machine learning. Their prototype learning scheme enables intuitive explanations of the form, this (prototype) look
Externí odkaz:
http://arxiv.org/abs/2309.14531
With the increasing reliance on small Unmanned Aerial Systems (sUAS) for Emergency Response Scenarios, such as Search and Rescue, the integration of computer vision capabilities has become a key factor in mission success. Nevertheless, computer visio
Externí odkaz:
http://arxiv.org/abs/2309.09518
Autor:
Theisen, William, Scheirer, Walter
The interplay between the image and comment on a social media post is one of high importance for understanding its overall message. Recent strides in multimodal embedding models, namely CLIP, have provided an avenue forward in relating image and text
Externí odkaz:
http://arxiv.org/abs/2309.03921
Autor:
Abraham, Sophia J., Maduranga, Kehelwala D. G., Kinnison, Jeffery, Carmichael, Zachariah, Hauenstein, Jonathan D., Scheirer, Walter J.
Machine learning has achieved remarkable success over the past couple of decades, often attributed to a combination of algorithmic innovations and the availability of high-quality data available at scale. However, a third critical component is the fi
Externí odkaz:
http://arxiv.org/abs/2308.03317