Italy


The recent progress of deep convolutional neural networks has enabled great success in single image super-resolution (SISR) and many other vision tasks. Their performances are also being increased by deepening the networks and developing more sophisticated network structures. However, finding an optimal structure for the given problem is a difficult task, even for human experts. For this reason, neural architecture search (NAS) methods have been introduced, which automate the procedure of constructing the structures. In this paper, we expand the NAS to the super-resolution domain and find a lightweight densely connected network named DeCoNASNet. We use a hierarchical search strategy to find the best connection with local and global features. In this process, we define a complexity-based penalty for solving image super-resolution, which can be considered a multi-objective problem. Experiments show that our DeCoNASNet outperforms the state-of-the-art lightweight super-resolution networks designed by handcraft methods and existing NAS-based design.

ICPR 2020

1325 - Neural Architecture Search for Image Super-Resolution Using Densely Connected Search Space: DeCoNAS

neural architecture search(nas)

single image super resolution (sisr)

image restoration

ICPR 2020 is the premier international conference on Pattern Recognition, gathering leading researchers from around the world to present their latest achievements in Pattern Analysis and Recognition, applied Artificial Intelligence and Machine Learning. The topic of the year is " Putting Artificial intelligence to work on Patterns". 
For the first time, ICPR has employed a two-round review process similar to journal submissions, with new papers allowed to be submitted in either the first or second round and papers submitted in the first round and not accepted allowed to be revised and re-submitted for second round review. 

In this edition of the **ICPR 2020** Conference, there are 1407 Oral/Poster presentations organized in five Tracks: 
- Artificial Intelligence, Machine Learning for Pattern Analysis; 
- Biometrics, Human Analysis and Behavior Understanding; Computer Vision, Robotics and Intelligent Systems; 
- Document and Media Analysis; and 
- Image and Signal Processing. 

The **ICPR 2020** Main Conference program also includes 7 Keynotes from prominent world-renowned scientists, 8 Tutorials, 28 Demos, 8 Challenges, 2 Panels, as well as an Industry/Sponsor Exhibition. In addition, 41 Workshops on a range of important current and emerging research topics complete the ICPR 2020 program. 


poster

The “2019 Automatic Speaker Verification Spoofing And Countermeasures Challenge” (ASVspoof) competition aimed to facilitate the design of highly accurate voice spoofing attack detection systems. the competition did not emphasize model
complexity and latency requirements; such constraints are strict and integral in real-world deployment. Hence, most of the top performing solutions from the competition all used an ensemble approach, and combined multiple complex deep learning models to maximize detection accuracy – this kind of approach would sit uneasily with real-world deployment constraints. To design a lightweight system, we combined the notions of skip connection (from ResNet) and max feature map (from Light CNN), and evaluated the accuracy of the system using the ASVspoof 2019
dataset. With an optimized constant Q transform (CQT) feature, our single model achieved a replay attack detection equal error rate (EER) of 0.37% on the evaluation set, surpassing the top ensemble system from the competition that achieved an EER of 0.39%

ResMax: Detecting Voice Spoofing Attacks with Residual Network and Max Feature Map


In recent years, cross-media retrieval has drawn
considerable attention due to the exponential growth of multimedia
data. Many hashing approaches have been proposed for the
cross-media search task. However, there are still open problems
that warrant investigation. For example, most existing supervised
hashing approaches employ a binary label matrix, which achieves
small margins between wrong labels (0) and true labels (1). This
may affect the retrieval performance by generating many false
negatives and false positives. In addition, some methods adopt
a relaxation scheme to solve the binary constraints, which may
cause large quantization errors. There are also some discrete
hashing methods that have been presented, but most of them are
time-consuming. To conquer these problems, we present a label
relaxation and discrete matrix factorization method (LRMF) for
cross-modal retrieval. It offers a number of innovations. First
of all, the proposed approach employs a novel label relaxation
scheme to control the margins adaptively, which has the benefit of
reducing the quantization error. Second, by virtue of the proposed
discrete matrix factorization method designed to learn the binary
codes, large quantization errors caused by relaxation can be
avoided. The experimental results obtained on two widely-used
databases demonstrate that LRMF outperforms state-of-the-art
cross-media methods.

Fast Discrete Cross-Modal Hashing Based on Label Relaxation and Matrix Factorization

Visual voice activity detection (V-VAD) uses visual features to predict whether a person is speaking or not. VVAD is useful whenever audio VAD (A-VAD) is inefficient either because the acoustic signal is difficult to analyze or because it is simply missing. We propose two deep architectures for V-VAD, one based on facial landmarks and one based on optical flow. Moreover, available datasets, used for learning and for testing VVAD, lack content variability. We introduce a novel methodology to automatically create and annotate very large datasets inthe-wild – WildVVAD – based on combining A-VAD with face detection and tracking. A thorough empirical evaluation showsthe advantage of training the proposed deep V-VAD models with this dataset.

Learning Visual Voice Activity Detection with an Automatically
Annotated Dataset


In this paper we benchmark different types of
cameras and evaluate their performance in terms of reliable
localization reliability and precision in Visual Simultaneous
Localization and Mapping (vSLAM). Such benchmarking is
merely found for visual odometry, but never for vSLAM. Existing
studies usually compare several algorithms for a given camera.
The evaluation methodology we propose is applied to the recent
OpenVSLAM framework. The latter is versatile enough to natively
deal with perspective, fisheye, 360 cameras in a monocular or
stereoscopic setup, an in RGB or RGB-D modalities. Results
in various sequences containing light variation and scenery
modifications in the scene assess quantitatively the maximum
localization rate for 360 vision. In the contrary, RGB-D vision
shows the lowest localization rate, but highest precision when
localization is possible. Stereo-fisheye trades-off with localization
rates and precision between 360 vision and RGB-D vision.
The dataset with ground truth will be made available in open
access to allow evaluating other/future vSLAM algorithms with
respect to these camera types.

Benchmarking Cameras for OpenVSLAM Indoors​


This paper studies the problem of Person Re-Identification (ReID)
for large-scale applications. Recent research efforts have been devoted
to building complicated part models, which introduce considerably
high computational cost and memory consumption, inhibiting its practicability
in large-scale applications. This paper aims to develop a novel learning
strategy to find efficient feature embeddings while maintaining the
balance of accuracy and model complexity. More specifically, we find
by enhancing the classical triplet loss together with cross-entropy
loss, our method can explore the hard examples and build a discriminant
feature embedding yet compact enough for large-scale applications.
Our method is carried out progressively using Bayesian optimization,
and we call it the Progressive Learning Algorithm (PLA). Extensive
experiments on three large-scale datasets show that our PLA is comparable
or better than the state-of-the-arts. Especially, on the challenging
Market-1501 dataset, we achieve Rank-1=94.7%/mAP=89.4% while saving
at least 30\% parameters than strong part models.

Can Reinforcement Learning Lead to Healthy Life?: Simulation Study Based on User Activity Logs

Local feature descriptors exhibit great superiority in finger vein recognition due to their stability and robustness against local changes in images. However, most of these are methods use general-purpose descriptors that do not consider finger veinspecific features. In this work, we propose a finger vein-specific local feature descriptors based physiological characteristic of finger vein patterns, i.e., histogram of oriented physiological Gabor responses (HOPGR), for finger vein recognition. First, a prior of directional characteristic of finger vein patterns is obtained in an unsupervised manner. Then the physiological Gabor filter banks are set up based on the prior information to extract the physiological responses and orientation. Finally, to make the feature robust against local changes in images, a histogram is generated as output by dividing the image into non-overlapping cells and overlapping blocks. Extensive experimental results on several databases clearly demonstrate that the proposed method outperforms most current state-of-the-art finger vein recognition methods.

A Local Descriptor with Physiological Characteristic
for Finger Vein Recognition

Elastic Weight Consolidation (EWC) is a technique used in overcoming catastrophic forgetting between successive tasks trained on a neural network. We use this phenomenon of information sharing between tasks for domain adaptation. Training data for tasks such as sentiment analysis (SA) may not be fairly represented across multiple domains. Domain Adaptation (DA) aims to build algorithms that leverage information from source domains to facilitate performance on an unseen target domain. We propose a model-independent framework- Sequential Domain Adaptation (SDA). SDA draws on EWC for training on successive source domains to move towards a general domain solution, thereby solving the problem of domain adaptation

Sequential Domain Adaptation through Elastic Weight Consolidation for Sentiment Analysis

Learning graph convolutional networks (GCNs) is an emerging field which aims at generalizing deep learning to arbitrary non-regular domains. Most of the existing GCNs follow a neighborhood aggregation scheme, where the representation of a node is recursively obtained by aggregating its neighboring node representations using averaging or sorting operations. However, these operations are either ill-posed or weak to be discriminant or increase the number of training parameters and thereby the computational complexity and the risk of overfitting. 
In this paper, we introduce a novel GCN framework that achieves spatial graph convolution in a reproducing kernel Hilbert space. The latter makes it possible to design, via implicit kernel representations, convolutional graph filters in a high dimensional and more discriminating space without increasing the number of training parameters. The particularity of our GCN model also resides in its ability to achieve convolutions without explicitly realigning nodes in the receptive fields of the learned graph filters with those of the input graphs, thereby making convolutions permutation agnostic and well defined. Experiments conducted on the challenging task of skeleton-based action recognition show the superiority of the proposed method against different baselines as well as the related work.

Kernel-based Graph Convolutional Networks

We propose a novel supervised machine learning strategy, inspired by human learning, that enables an Agent to learn continually over its lifetime. A natural consequence is that the Agent must be able to handle an input whose label is delayed until a later time, or may not arrive at all. Our Agent learns in two steps: a short Seeding phase, in which the Agent’s model is initialized with labelled inputs, and an indefinitely long Growing phase, in which the Agent refines and assesses its model if the label is given for an input, but stores the input in a finite- length queue if the label is missing. Queued items are matched against future input-label pairs that arrive, and the model is then updated. Our strategy also allows for the delayed feedback to take a different form. For example, in an image captioning task, the feedback could be a semantic segmentation rather than a textual caption. We show with many experiments that our strategy enables an Agent to learn flexibly and efficiently.

Learning with Delayed Feedback

We present an approach to detecting small cell populations in flow cytometry (FCM) samples based on the combination of unsupervised manifold embedding and supervised random forest classification. Each sample consists of hundred thousands to a million cells where each cell typically corresponds to a measurement vector with 10 to 50 dimensions. The difficulty of the task is that clusters of measurement vectors formed in the data space according to standard clustering criteria often do not correspond to biologically meaningful sub-populations of cells, due to strong variations in shape and size of their distributions. In many cases the relevant population consists of less than 100 scattered events out of millions of events, where supervised approaches perform better than unsupervised clustering. The aim of this paper is to demonstrate that the performance of the standard supervised classifier can be improved significantly by combining it with a preceding unsupervised learning step involving the Uniform Manifold Approximation and Projection (UMAP). We present an experimental evaluation on FCM data from children suffering from Acute Lymphoblastic Leukemia (ALL) showing that the positive effect of the UMAP becomes more noticeable for smaller training sets. Further, the experiments indicate that in this situation the algorithm also outperforms other baseline methods based on Gaussian Mixture Models.

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