TA7a.4

Efficient Methods for Modeling Shift-Varying Operators

Nebiyou Yismaw, University of California Riverside, United States; Chinmay Hegde, New York University, United States; M. Salman Asif, University of California Riverside, United States
Session:
TA7a: Efficient Learning and Adaptation Methods Lecture
Track:
Speech, Image and Video Processing
Location:
Room 7
Presentation Time:
Tue, 28 Oct, 09:30 - 09:55 PT (UTC -7)
Presentation
Discussion
Resources
No resources available.
Session TA7a
TA7a.1: Adaptive Additive Parameter Updates of Vision Transformers for Few-Shot Continual Learning
Kyle Stein, The University of West Florida, United States; Andrew Arash Mahyari, Institute for Human and Machine Cognition, United States; Guillermo Francia, Eman El-Sheikh, The University of West Florida, United States
TA7a.2: Cross-Dataset, Age, and Gender Generalization: A Comprehensive Analysis of Fine-Tuning Strategies for Low-Resource Children’s ASR
Abhijit Sinha, Hemant Kumar Kathania, NIT Sikkim, India; Sudarsana Reddy Kadiri, Shrikanth Narayanan, University of Southern California, United States
TA7a.3: PATCH-BASED IMAGE DENOISING VIA AFFINE SUBSPACE MODEL CLUSTERING
Tharindu Wickremasinghe, Purdue University, United States; Marco Duarte, University of Massachusetts-Amherst, United States
TA7a.4: Efficient Methods for Modeling Shift-Varying Operators
Nebiyou Yismaw, University of California Riverside, United States; Chinmay Hegde, New York University, United States; M. Salman Asif, University of California Riverside, United States
Contacts