Italy

Applying Global Self-attention (GSA) mechanism over features has achieved remarkable success on Convolutional Neural Networks (CNNs). However, it is not clear if Graph Convolutional Networks (GCNs) can similarly benefit from such a technique. In this work, inspired by the similarity between CNNs and GCNs, we study the impact of the Global Self-attention mechanism on GCNs. We find that consistent with the intuition, the GSA mechanism allows GCNs to capture feature-based vertex relations regardless of edge connections; As a result, the GSA mechanism can introduce extra expressive power to the GCNs. Furthermore, we analyze the impacts of the GSA mechanism on the issues of overfitting and over-smoothing. We prove that the GSA mechanism can alleviate both the overfitting and the over-smoothing issues based on some recent technical developments. Experiments on multiple benchmark datasets illustrate both superior expressive power and less significant overfitting and over-smoothing problems for the GSA-augmented GCNs, which corroborate the intuitions and the theoretical results.

ICPR 2020

On the Global Self-attention Mechanism for Graph Convolutional Networks

over-fitting

over-smoothing

graph convolutional networks

self-attention

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

Generative adversarial networks (GANs) are deep neural networks that allow us to sample from an arbitrary probability distribution without explicitly estimating the distribution. There is a generator that takes a latent vector as input and transforms it into a valid sample from the distribution. There is also a discriminator that is trained to discriminate such fake samples from true samples of the distribution; at the same time, the generator is trained to generate fakes that the discriminator cannot tell apart from the true samples. Instead of learning a global generator, a recent approach involves training multiple generators each responsible from one part of the distribution. In this work, we review such approaches and propose the hierarchical mixture of generators, inspired from the hierarchical mixture of experts model, that learns a tree structure implementing a hierarchical clustering with soft splits in the decision nodes and local generators in the leaves. Since the generators are combined softly, the whole model is continuous and can be trained using gradient-based optimization, just like the original GAN model. Our experiments on five image data sets, namely, MNIST, FashionMNIST, UTZap50K, Oxford Flowers, and CelebA, show that our proposed model generates samples of high quality and diversity in terms of popular GAN evaluation metrics. The learned hierarchical structure also leads to knowledge extraction.

Hierarchical Mixtures of Generators for Adversarial Learning

Convolutional Neural Networks (CNNs) have recently been introduced for addressing copy-move forgery detection (CMFD). However, current CMFD CNN-based approaches have insufficient performance commitment regarding the 
localization of the positive class. In this paper, this issue is explored by considering both linear and nonlinear interactions between pixels. A nonlinear Inception module based on second-order Volterra kernels is proposed, in order to ameliorate the results of a state-of-the-art CMFD architecture. The outcome of this work shows that a combination of linear and nonlinear convolution kernels can make the input foreground and background pixels more separable. The proposed approach is evaluated on CASIA and CoMoFoD, two publicly available CMFD datasets, and
results to an improved positive class localization performance. Moreover, the findings of the proposed method imply that the nonlinear Inception module stimulates immense robustness against miscellaneous post processing attacks.

RobusterNet: Improving Copy-Move Forgery Detection with Volterra-based Convolutions

Mutual information (MI) is an information-theoretic measure of dependency between two random variables. Several methods to estimate MI, from samples of two random variables with unknown underlying probability distributions have been
proposed in the literature. Recent methods realize parametric probability distributions or critic as a neural network to approximate unknown density ratios. The approximated density ratios are used to estimate different variational lower bounds of MI. While these methods provide reliable estimation when the true MI is low, they produce high variance estimates in cases of high MI. We argue that the high variance characteristic is due to the uncontrolled complexity of the critic's hypothesis space. In support of this argument, we use the data-driven Rademacher complexity of the hypothesis space associated with the critic's architecture to analyse generalization error bound of variational lower bound estimates of MI. In the proposed work, we show that it is possible to negate the high variance characteristics of these estimators by constraining the critic's hypothesis space to Reproducing Hilbert Kernel Space (RKHS), which corresponds to a kernel learned using Automated Spectral Kernel Learning (ASKL). By analysing the aforementioned generalization error bounds, we augment the overall optimisation objective with effective regularisation term. We empirically demonstrate the efficacy of this regularization in enforcing proper bias variance tradeoff on four variational lower bounds, namely NWJ, MINE, JS and SMILE.

Reducing the Variance of Variational Estimates of Mutual Information by Limiting the Critic's Hypothesis Space to RKHS

The ability to handle large scale variations is crucial for many real world visual tasks. A straightforward approach for handling scale in a deep network is to process an image at several scales simultaneously in a set of scale channels. Scale invariance can then, in principle, be achieved by using weight sharing between the scale channels together with max or average pooling over the outputs from the scale channels. The ability of such scale channel networks to generalise to scales not present in the training set over significant scale ranges has, however, not previously been explored. We, therefore, present a theoretical analysis of invariance and covariance properties of scale channel networks and perform an experimental evaluation of the ability of different types of scale channel networks to generalise to previously unseen scales. We identify limitations of previous approaches and propose a new type of foveated scale channel architecture, where the scale channels process increasingly larger parts of the image with decreasing resolution. Our proposed FovMax and FovAvg networks perform almost identically over a scale range of 8, also when training on single scale training data, and do also give improvements in the small sample regime.

Exploring the ability of CNNs to generalise to previously unseen scales over wide scale ranges

Regression via classification (RvC) is a common method used for regression problems in deep learning, where the target variable belongs to a set of continuous values. By discretizing the target into a set of non-overlapping classes, it has been shown that training a classifier can improve neural network accuracy compared to using a standard regression approach. However, it is not clear how the set of discrete classes should be chosen and how it affects the overall solution. In this work, we propose that using several discrete data representations simultaneously can improve neural network learning compared to a single representation. Our approach is end-to-end differentiable and can be added as a simple extension to conventional learning methods, such as deep neural networks. We test our method on three challenging tasks and show that our method reduces the prediction error compared to a baseline RvC approach while maintaining a similar model complexity.

Deep Ordinal Regression with Label Diversity

We train a detection, classification and score analysis DNN to improve fencing referee decisions and provide trainers statistical analysis. The model classifies ‘Lunges’, ‘Counter-Attack’, and ‘Preparation to attack’ and introduces additional classes to reduce false positives. A scoring function evaluates, in real-time, who is awarded points.

Real-Time Fencing Move Classification and Detection at Touch Time during a Fencing Match

This work considers the problem of domain shift in person re-identification. Being trained on one dataset, a re-identification model usually performs much worse on unseen data. Partially this gap is caused by the relatively small scale of person re-identification datasets (compared to face recognition ones, for instance), but it is also related to training objectives. We propose to use the metric learning objective, and some additional training practices to build well-generalizing, yet, computationally efficient models.

Building Computationally Efficient and Well-Generalizing Person Re-Identification Models with Metric Learning

We present a novel approach of 2D to 3D transfer learning for semantic segmentation based on mapping pre-trained 2D convolutional neural network weights into planar 3D kernels. Results were evaluated using a public dataset for multiple sclerosis lesion segmentation utilizing solely MRI Flair sequence without brain extraction. Complete source code is available under an open-source license.

Planar 3D Transfer Learning for End to End Unimodal MRI Unbalanced Data Segmentation

We propose a novel foreground-focused domain adaptation (FFDA) framework
which mines the loss of the domain discriminators to concentrate on the backpropagation of foreground loss.

Foreground-focused domain adaptation for object detection

As one of the primary tasks in data mining, outlier detection serves a significant role in data quality enhancement for the scientific model prediction and revealing the abnormal hidden patterns from large scale trajectory datasets. In this paper, we introduce a versatile framework for detecting local trajectory outliers using spatial and temporal features of moving objects. Our local outlier detection consists of three phases. First, we divide the raw trajectory into trajectory segments by using a time-based partition strategy and extracting trajectory features from spatial attributes for each trajectory segment. Second, we create template trajectory segments based on a clustering schema. Finally, we compute the abnormal score, which measures the dissimilarity among the query and template trajectory segments, and thus determine the outlying trajectory segments according to the overall distribution of the abnormal score. To show the effectiveness of our approach, we conduct two case studies on the real-life solar active region and Coronal Mass Ejection (CME) trajectory datasets. Our results show that our local outlier detection method can successfully detect the reporting errors and anomalous phenomenon in both of our case studies.

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