Special Track: Generalization in Visual Machine Learning Call for Papers
Special Track: Generalization in Visual Machine Learning
19th International Symposium on Visual Computing
Lake Tahoe, NV, USA
October 21-23, 2024
http://www.isvc.net
Scope: Generalization is particularly important in machine learning
for visual computing due to the complex and diverse nature of visual
data. In visual computing, machine learning models are often trained
on large datasets of images or videos with the goal of performing
tasks such as object recognition, segmentation, classification, or
detection. Achieving good generalization is crucial for the practical
utility of these models as they need to perform accurately on new,
unseen images or videos. Good generalization is especially important
in real-world applications where visual data can vary widely in
appearance, context, and lighting conditions.
Another reason why generalization is important in machine learning
for visual computing is the potential for bias and
overfitting. Visual datasets are often biased towards specific
classes, contexts, or viewpoints, which can lead to poor
generalization when models are applied to new data outside of these
biases. Additionally, machine learning models trained on visual data
can easily overfit to noise or irrelevant features in the training
data, leading to poor performance on new data.
To address these challenges, researchers in machine learning for
visual computing have developed a range of techniques to improve
generalization. These include regularization techniques to prevent
overfitting, transfer learning and domain adaptation techniques to
leverage pre-trained models or adapt to new domains, data augmentation
techniques to increase the diversity of the training data, and
uncertainty estimation techniques to quantify model confidence and
detect potential errors.
We invite research contributions to this special issue on
Generalization in Visual Machine Learning. We welcome original
research articles, reviews, and survey papers on the above
topics. All submissions will be rigorously peer-reviewed and selected
based on their relevance, technical quality, and originality.
Topics: Topics of interest include but are not limited to:
Regularization techniques for improving generalization in visual computing
Novel hierarchical architecture for domain generalization
Transfer learning and domain adaptation for visual computing
Data augmentation and synthesis techniques for improving
generalization in visual computing
Uncertainty estimation in visual computing
Generalization in aerial surveillance under complex and contested
environments
Generalization in object detection
Robustness and adversarial attacks in visual computing
Explainability and interpretability of visual computing models
Novel approaches for improving generalization in visual computing
Generalization in visual object tracking
Generalization in biometric recognition techniques
Generalization on medical image segmentation
Zero-shot learning for visual computing
Disentangled representations for improving generalization in
visual computing
Graph based approaches (Graph Signal Processing, Graph Neural
Networks) in visual computing
Organizers:
Mohamed S. Shehata, University of British Columbia, BC, Canada
Minglun Gong, University of Guelph, Ontario, Canada
Thierry Bouwmans, La Rochelle Université, La Rochelle, France.
Ahmed R. Hussein, University of Guelph, Ontario, Canada
Paola Barra, Università degli studi di Napoli « Parthenope », Italy
Deepak Kumar Jain, University of Chinese Academy of Sciences, China,
Soon Ki Jung, Kyungpook National University, South Korea
Important Dates:
Same as ISVC deadlines. Please visit: http://www.isvc.net/
Paper Submission Instructions
Same as ISVC paper submission instructions, see
http://www.isvc.net/index.php/paper-submission/