This workshop aims at promoting discussions among researchers investigating innovative tensor-based approaches to computer vision problems. Tensors have been a crucial mathematical object for several applications in computer vision and machine learning. It has been an essential ingredient in modelling latent semantic spaces, higher-order data factorization, and modelling higher-order information in visual data, and has found numerous applications in several hot topics in computer vision including, but not limited to human action recognition, object recognition, and video understanding. Moreover, tensor-based algorithms are increasingly finding significant applications in deep learning. With the rise of big data, tensors may yet prove crucial in both understanding deep architectures, as well as, may aid robust learning and generalization in inference algorithms.
We are soliciting original contributions that address a wide range of theoretical and practical issues including, but not limited to:
Tensor methods in deep learning
Supervised learning in computer vision
Unsupervised feature learning and multimodal representations
Tensors in low-level feature design
Mid-level representations with tensor methods
Low-rank factorisation methods and denoising approaches
Latent topic models using tensor methods
Tensors in optimization and dictionary learning
Tensor hardware architectures
Advancements in multi-linear algebra
Riemannian geometry and SPD matrices
Applications of tensors for:
image/video recognition
object recognition
scene understanding
industrial and medical applications
07月26日
2017
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