Special Track: Generalization in Visual Machine Learning

19^th 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, Dalian University of Technology, 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/

CfP: 9th international Workshop on Sensor-Based Activity Recognition and Artificial Intelligence (iWOAR 2024)

iWOAR is an international workshop with conference character, which is typically organised in-cooperation with ACM. This year's workshop will be held in Potsdam/Berlin (Germany) at the Hasso Plattner Institute (HPI) during September 26-27, 2024. The workshop focuses on Human Activity Recognition and Artificial Intelligence with wearable sensors and related technologies. This year’s symposium will feature as keynote speaker the presence of Prof. Dr.  Thomas Plötz from Georgia Institute of Technology. As a leading expert, his research and innovative approaches have significantly advanced the field of computational behavior analysis and activity recognition.

Paper submission deadline: June 15, 2024
Poster submission deadline: August 1, 2024
Website: https://iwoar.org/2024/
Paper submission link: https://easychair.org/my/conference?conf=iwoar2024

All scientific submissions undergo a peer review process to ensure that high quality content is being presented. We invite submissions which target (but are not limited to) the following topics and applications:

 Human Activity Recognition
 Affective Computing
 Artificial intelligence for wearables
 Real-time Activity Recognition
 Synthetic Data Generation
 Assistive Technologies
 Privacy and Ethical Considerations in Activity Recognition
 Pervasive & Wearable Computing
 Personalized Diagnostics and Therapy
 Quantified Self / Biofeedback
 Applications for (Mental) Health, Therapy and Rehabilitation
 Design and Conceptual Innovations in Sensor Networks

We invite for academic research papers and for best-practice industrial research approaches. iWOAR 2024 will feature both a paper track and a poster track, with all submissions being handled via EasyChair (submission link: https://easychair.org/my/conference?conf=iwoar2024):

• Paper track: submissions must follow the ACM SIGCHI Paper Format. The submission deadline is set to June 15, 2024.
• Poster track: submissions must follow the ACM SIGCHI Extended Abstract Format. The submission deadline is set to August 1, 2024.

Accepted submissions to the paper track are scheduled to be published in the iWOAR 2024 Proceedings online in the ACM Digital Library (https://dl.acm.org/conference/iwoar).