26 research outputs found
Memory-Efficient Deep Salient Object Segmentation Networks on Gridized Superpixels
Computer vision algorithms with pixel-wise labeling tasks, such as semantic
segmentation and salient object detection, have gone through a significant
accuracy increase with the incorporation of deep learning. Deep segmentation
methods slightly modify and fine-tune pre-trained networks that have hundreds
of millions of parameters. In this work, we question the need to have such
memory demanding networks for the specific task of salient object segmentation.
To this end, we propose a way to learn a memory-efficient network from scratch
by training it only on salient object detection datasets. Our method encodes
images to gridized superpixels that preserve both the object boundaries and the
connectivity rules of regular pixels. This representation allows us to use
convolutional neural networks that operate on regular grids. By using these
encoded images, we train a memory-efficient network using only 0.048\% of the
number of parameters that other deep salient object detection networks have.
Our method shows comparable accuracy with the state-of-the-art deep salient
object detection methods and provides a faster and a much more memory-efficient
alternative to them. Due to its easy deployment, such a network is preferable
for applications in memory limited devices such as mobile phones and IoT
devices.Comment: 6 pages, submitted to MMSP 201
Stiffness constants prediction of nanocomposites using a periodic 3D-FEM model
Predictive models, which enable the prediction of nanocomposite properties from their morphologies and account for polymer orientation, could greatly assist the exploitation of this new class of materials in more diversified and demanding market fields. This article focuses on the prediction of effective elastic properties (Young's moduli) of polymer nanocomposite films (copolyamide-6/nanoclay) using 3D analytical (based on the Mori-Tanaka theory) and 3D finite element (FE) models. The analytical model accounts for the orientation of polymer chains induced by drawing. 3D FE model exploits the representative volume element concept and accounts for the nanocomposite morphology as determined from transmission electron microscopy experiments. Model predictions were compared with experimental results obtained for nanocomposite films produced by means a pilot-scale film blowing equipment and collected at different draw ratios
Leveraging progressive model and overfitting for efficient learned image compression
Deep learning is overwhelmingly dominant in the field of computer vision and
image/video processing for the last decade. However, for image and video
compression, it lags behind the traditional techniques based on discrete cosine
transform (DCT) and linear filters. Built on top of an autoencoder
architecture, learned image compression (LIC) systems have drawn enormous
attention in recent years. Nevertheless, the proposed LIC systems are still
inferior to the state-of-the-art traditional techniques, for example, the
Versatile Video Coding (VVC/H.266) standard, due to either their compression
performance or decoding complexity. Although claimed to outperform the
VVC/H.266 on a limited bit rate range, some proposed LIC systems take over 40
seconds to decode a 2K image on a GPU system. In this paper, we introduce a
powerful and flexible LIC framework with multi-scale progressive (MSP)
probability model and latent representation overfitting (LOF) technique. With
different predefined profiles, the proposed framework can achieve various
balance points between compression efficiency and computational complexity.
Experiments show that the proposed framework achieves 2.5%, 1.0%, and 1.3%
Bjontegaard delta bit rate (BD-rate) reduction over the VVC/H.266 standard on
three benchmark datasets on a wide bit rate range. More importantly, the
decoding complexity is reduced from O(n) to O(1) compared to many other LIC
systems, resulting in over 20 times speedup when decoding 2K images