Italy




This paper studies the indoor localisation of WiFi devices based on a commodity chipset and standard channel sounding. First, we present a novel shallow neural network (SNN) in which features are extracted from the channel state information (CSI) corresponding to WiFi subcarriers received on different antennas and used to train the model. The single-layer architecture of this localisation neural network makes it lightweight and easy-to-deploy on devices with stringent constraints on computational resources. We further investigate for localisation the use of deep learning models and design novel architectures for convolutional neural network (CNN) and long-short term memory (LSTM). We extensively evaluate these
localisation algorithms for continuous tracking in indoor environments. Experimental results prove that even an SNN model, after a careful handcrafted feature extraction, can achieve accurate localisation. Meanwhile, using a well-organised architecture, the neural network models can be trained directly with raw data from the CSI and localisation features can be automatically extracted to achieve accurate position estimates. We also found that the performance of neural network-based methods are directly affected by the number of anchor access points (APs) regardless of their structure. With three APs, all neural network models proposed in this paper can obtain localisation accuracy of around 0.5 metres. In addition the proposed deep NN architecture reduces the data pre-processing time by 6.5 hours compared with a shallow NN using the data collected in our testbed. In the deployment phase, the inference time is also significantly reduced to 0.1 ms per sample. We also demonstrate the generalisation capability of the proposed method by evaluating models using different target movement characteristics to the ones in which they were trained.


ICPR 2020

Wireless Localisation in WiFi using Novel Deep Architectures

wireless localisation

lstm

The International Conference on Pattern Recognition Applications and Methods is a major point of contact between researchers, engineers and practitioners on the areas of Pattern Recognition and Machine Learning, both from theoretical and application perspectives.<br><br>CONFERENCE AREAS<br>
1 . Theory and Methods<br>
2 . Applications

ICPRAM 2022

The International Conference on Pattern Recognition Applications and Methods is a major point of contact between researchers, engineers and practitioners on the areas of Pattern Recognition and Machine Learning, both from theoretical and application perspectives.

poster

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. 


In class-incremental learning, a learning agent faces a stream of data with the goal of learning new classes while not forgetting previous ones. Neural networks are known to suffer under this setting, as they forget previously acquired knowledge. To address this problem, effective methods exploit past data stored in an episodic memory while expanding the final classifier nodes to accommodate the new classes.

In this work, we substitute the expanding classifier with a novel fixed classifier
in which a number of pre-allocated output nodes are subject to the classification loss right from the beginning of the learning phase. Contrarily to the standard expanding classifier, this allows: (a) the output nodes of future unseen classes to firstly see negative samples since the beginning of learning together with the positive samples that incrementally arrive; (b) to learn features that do not change their geometric configuration as novel classes are incorporated in the learning model.

Experiments with public
datasets show that the proposed approach is as effective as the expanding classifier while exhibiting novel intriguing properties of the internal feature representation that are otherwise not-existent. Our ablation study on pre-allocating a large number of classes further validates the approach.

Class-incremental Learning with Regular Polytope Networks

In this presentation, the Surface Material Dataset for Robotics Applications (SMDRA) is introduced including the necessity for this dataset and how the data was collected and verified.

Surface Material Dataset for Robotics Applications (SMDRA): A Dataset with Friction Coefficient and RGB-D for Surface Segmentation


Representational Learning in the form of high dimensional embeddings has been used for multiple pattern recognition applications. 
There has been a significant interest in building embedding based systems for learning representations in the mathematical domain. 
At the same time, retrieval of structured information such as mathematical expressions is an important need for modern IR systems.
In this work, our motivation is to introduce a robust framework for learning representations 
for similarity-based retrieval of mathematical expressions.
Given a query by example, the embedding can find the closest matching expression as a function of Euclidean distance between them. 
We leverage recent advancements in image-based and graph-based deep learning algorithms to learn our similarity embeddings. 
We do this first, by using uni-modal encoders in graph space and image space and then, by a multi-modal combination of the same. 
To overcome the lack of training data, we force the networks to learn a deep metric using triplets generated with a heuristic scoring function.
We also adopt a custom strategy for mining hard samples to train our neural networks. 
Our system produces rankings similar to those generated by the original scoring function, but using only a fraction of the time.
Our results establish the viability of using such a multi-modal embedding for this task.

Equation Attention Relationship Network (EARN) : A Geometric Deep Metric Framework for Learning Similar Math Expression Embedding

With more research published on adversarial examples, we face a growing need for strong and insightful methods for evaluating the robustness of machine learning solutions against their adversarial threats. Previous work contains problematic and overly simplified evaluation methods, where different methods for generating adversarial examples are compared, even though they produce adversarial examples of differing perturbation magnitudes. This creates a biased evaluation environment, as higher perturbations yield naturally stronger adversarial examples. We propose a novel "accuracy-perturbation curve" that visualizes a classifiers classification accuracy response to adversarial examples of different perturbations. To demonstrate the utility of the curve we perform evaluation of responses of different image classifier architectures to four popular adversarial example methods. We also show how adversarial training improves the robustness of a classifier using the "accuracy-perturbation curve".

Accuracy-Perturbation Curves for Evaluation of Adversarial Attack and Defence Methods

In this paper, we present a novel framework that uses tensor factorization to generate richer feature spaces for pixel classification in hyperspectral images. In
particular, we assess the performance of different tensor rank decomposition methods as compared to the traditional kernel-based approaches for the hyperspectral image classification problem. We propose ORION, which takes as
input a hyperspectral image tensor and a rank and outputs an enhanced feature space from the factor matrices of the decomposed tensor. Our method is a feature explosion technique that inherently maps low dimensional input space in R^K to high dimensional space in R^R, where R >> K, say in the order of 1000x, like a kernel. We show how the proposed method exploits the multi-linear structure of hyperspectral three-dimensional tensor. We demonstrate the effectiveness of our method with experiments on three publicly available hyperspectral datasets with labeled pixels and compare their classification performance against traditional linear and non-linear supervised learning methods such as SVM with Linear, Polynomial, RBF kernels, and the Multi-Layer Perceptron model. Finally, we explore the relationship between the rank of the tensor decomposition and the classification accuracy using several hyperspectral datasets with ground truth.

Tensorized Feature Spaces for Feature Explosion

The problem of uncovering different dynamical regimes is of pivotal importance in time series analysis. Switching dynamical systems provide a solution for modeling physical phenomena whose time series data exhibit different dynamical modes. In this work we propose a novel variational RNN model for switching dynamics allowing for both non-Markovian and non-linear dynamical behavior between and within dynamic modes. Attention mechanisms are provided to inform the switching distribution.
We evaluate our model on synthetic and empirical datasets of diverse nature and successfully uncover different dynamical regimes and predict the switching dynamics.

Switching Dynamical Systems with Deep Neural Networks

Early hierarchical computational visual models as
well as recent deep neural networks have been inspired by the
functioning of the primate visual cortex system. Although much
effort has been made to dissect neural networks to visualize
the features they learn at the individual units, the scope of
the visualizations has been limited to a categorization of the
features in terms of their semantic level. Considering the ability
humans have to select high semantic level regions of a scene,
the question whether neural networks can match this ability,
and if similarity with humans attention is correlated with neural
networks performance naturally arise. To address this question
we propose a pipeline to select and compare sets of feature points
that maximally activate individual networks units to human
fixations. We extract features from a variety of neural networks,
from early hierarchical models such as HMAX up to recent
deep convolutional neural netwoks such as Densnet, to compare
them to human fixations. Experiments over the ETD database
show that human fixations correlate with CNNs features from
deep layers significantly better than with random sets of points,
while they do not with features extracted from the first layers
of CNNs, nor with the HMAX features, which seem to have
low semantic level compared with the features that respond
to the automatically learned filters of CNNs. It also turns out
that there is a correlation between CNN’s human similarity and
classification performance.

From early biological models to CNNs: do they
look where humans look?

Automatic fall detection is a vital technology for ensuring health and safety of people. Home based camera systems for fall detection often put people’s privacy at risk. Thermal cameras can partially/fully obfuscate facial features, thus preserving the privacy of a person. Another challenge is the less occurrence of falls in comparison to normal activities of daily living. As fall occurs rarely, it is non-trivial to learn algorithms due to classimbalance. To handle these problems, we formulate fall detection as an anomaly detection within an adversarial framework using thermal imaging camera. We present a novel adversarial network that comprise of two channel 3D convolutional auto encoders; one each handling video sequences and optical flow, which then reconstruct the thermal data and the optical flow input sequences.We introduce a differential constraint, a technique to track the region of interest and a joint discriminator to compute there construction error. Larger reconstruction error indicates the occurrence of fall in a video sequence. The experiments on a publicly available thermal fall dataset show the superior results obtained in comparison to standard baseline.

Motion and Region Aware Adversarial Learning for Fall Detection with Thermal Imaging


Deep learning has achieved successful performance in representation learning and content-based retrieval of histopathology images. The commonly used setting in deep learning-based approaches is supervised training of deep neural networks for classification, and using the trained model to extract representations that are used for computing and ranking the distances between images. However, there are two remaining major challenges. First, supervised training of deep neural networks requires large amount of manually labeled data which is often limited in the medical field. Transfer learning has been used to overcome this challenge, but its success remained limited. Second, the clinical practice in histopathology necessitates working with regions of interest (ROI) of multiple diagnostic classes with arbitrary shapes and sizes. The typical solution to this problem is to aggregate the representations of fixed sized patches cropped from these regions to obtain region level representations. However, naive methods cannot sufficiently exploit the rich contextual information in the complex tissue structures. To tackle these two challenges, we propose a generic method that utilizes graph neural networks (GNN), combined with a self-supervised training method using a contrastive loss. GNN enables representing arbitrarily-shaped ROIs as graphs and encoding contextual information. Self-supervised contrastive learning improves quality of learned representations without requiring labeled data. The experiments using a challenging breast histopathology data set show that the proposed method achieves better performance than the state-of-the-art.

Self-Supervised Learning with Graph Neural Networks for Region of Interest Retrieval in Histopathology

This work studies Semantic Scene Completion which aims to predict a 3D semantic segmentation
of our surroundings, even though some areas are occluded.
For this we construct a Bayesian Convolutional Neural Network (BCNN), which is
not only able to perform the segmentation, but also predict model uncertainty.
This is an important feature not present in standard CNNs.

We show results for the Semantic Scene Completion task
where a category is introduced at test time on the SUNCG dataset.
In this complex task the Bayesian approach outperforms the standard CNN,
showing better Intersection over Union score and excels in mean Average Precision.
With the added benefit of having better calibrated scores and the ability to express model uncertainty.


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Class-incremental Learning with Regular Polytope Networks

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