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/