Italy


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.

ICPR 2020

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

mathematical information retrieval; semi-supervised learning; graph matching

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

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.


In Depth Semantic Scene Completion


In this work, we present a deep learning based framework to explore to replicate similar or even higher performance as costly LIDARs with our designed self-supervised network and a low-cost single-beam LIDAR.

Extending Single Beam Lidar To Full Resolution By Fusing with
Single Image Depth Estimation


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.

Social Network Analysis using Knowledge-Graph Embeddings and Convolution Operations

Accurate real-time object recognition from sensory data has long been a crucial and challenging task for autonomous driving. Even though deep neural networks (DNNs) have been widely applied in this area, their considerable processing latency, power consumption, as well as computational complexity have been challenging issues for real-time autonomous driving applications. In this paper, we propose an approach to address the real-time object recognition problem utilizing spiking neural networks (SNNs). The proposed SNN model works directly with raw LiDAR temporal pulses without the pulse-to-point cloud preprocessing procedure, which can significantly reduce delay and power consumption. Being evaluated on various datasets derived from LiDAR and dynamic vision sensor (DVS), including Sim LiDAR, KITTI, and DVS-barrel, our proposed model has shown remarkable time and power efficiency, while achieving comparable recognition performance as the state-of-the-art methods. This paper highlights the SNN's great potentials in autonomous driving and related applications. To the best of our knowledge, this is the first attempt to use SNN to perform time and energy efficient object recognition directly on LiDAR temporal pulses in the setting of autonomous driving.

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

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