992 research outputs found
Multimodal Side-Tuning for Document Classification
In this paper, we propose to exploit the side-tuning framework for multimodal
document classification. Side-tuning is a methodology for network adaptation
recently introduced to solve some of the problems related to previous
approaches. Thanks to this technique it is actually possible to overcome model
rigidity and catastrophic forgetting of transfer learning by fine-tuning. The
proposed solution uses off-the-shelf deep learning architectures leveraging the
side-tuning framework to combine a base model with a tandem of two side
networks. We show that side-tuning can be successfully employed also when
different data sources are considered, e.g. text and images in document
classification. The experimental results show that this approach pushes further
the limit for document classification accuracy with respect to the state of the
art.Comment: 2020 25th International Conference on Pattern Recognition (ICPR
Real-time self-adaptive deep stereo
Deep convolutional neural networks trained end-to-end are the
state-of-the-art methods to regress dense disparity maps from stereo pairs.
These models, however, suffer from a notable decrease in accuracy when exposed
to scenarios significantly different from the training set, e.g., real vs
synthetic images, etc.). We argue that it is extremely unlikely to gather
enough samples to achieve effective training/tuning in any target domain, thus
making this setup impractical for many applications. Instead, we propose to
perform unsupervised and continuous online adaptation of a deep stereo network,
which allows for preserving its accuracy in any environment. However, this
strategy is extremely computationally demanding and thus prevents real-time
inference. We address this issue introducing a new lightweight, yet effective,
deep stereo architecture, Modularly ADaptive Network (MADNet) and developing a
Modular ADaptation (MAD) algorithm, which independently trains sub-portions of
the network. By deploying MADNet together with MAD we introduce the first
real-time self-adaptive deep stereo system enabling competitive performance on
heterogeneous datasets.Comment: Accepted at CVPR2019 as oral presentation. Code Available
https://github.com/CVLAB-Unibo/Real-time-self-adaptive-deep-stere
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