Italy

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 &gt;&gt; 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.

ICPR 2020

Tensorized Feature Spaces for Feature Explosion

hyperspectral images

applications of pattern recognition and machine learning

tensor decomposition

The purpose of the AAAI conference is to promote research in artificial intelligence (AI) and scientific exchange among AI researchers, practitioners, scientists, and engineers in affiliated disciplines. 

AAAI 2021

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.

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. 


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.

Temporal Pulses Driven Spiking Neural Network for Time and Power Efficient Object Recognition in Autonomous Driving

As with many problems solved by deep neural networks, existing solutions rarely explain, precisely, the important factors responsible for the predictions made by the model. This work looks to investigate how different spatial regions and landmark points change in position over time, to better explain the underlying factors responsible for various facial emotion expressions. By pinpointing the specific regions or points responsible for the classification of a particular facial expression, we gain better insight into the dynamics of the face when displaying that emotion. To accomplish this, we examine two spatiotemporal representations of moving faces, while expressing different emotions. The representations are then presented to a convolutional neural network for emotion classification. Class activation maps are used in highlighting the regions of interest and the results are qualitatively compared with the well known facial action units, using the facial action coding system. 
The model was originally trained and tested on the CK+ dataset for emotion classification, and then generalized to the SAMM dataset. In so doing, we successfully present an interpretable technique for understanding the dynamics that occur during convolutional-based prediction tasks on sequences of face data.

Interpreting Emotion Classification Using Temporal Convolutional Models


We present a new measure of dependence suitable for time series forecasting: Partial Monotone Correlation (PMC) that generalizes Monotone Correlation. Unlike the Monotone Correlation, the new measure of dependence uses piecewise strictly monotone transformations that increase the value of the correlation coefficient. We explore its properties, its relationship with Monotone and Maximal Correlation, and present an algorithm that calculates it based on the Simultaneous Perturbation Stochastic Approximation method. We also demonstrate how to apply Partial Monotone Correlation for time series analysis and forecasting introducing Partial Monotone Autoregressive model (PMAR) of order 1. Its performance is then evaluated against the baseline of linear and nonlinear autoregressive models (AR, LSTAR) on 150 time series produced from 3 datasets: Yellow Taxi pickups, Citi Bike pickups, and Cellular Network hits. Overall, PMAR model outperforms the baseline with the average sMAPE of about 1.7-4% lower.

Premium content

Downloads

Next from ICPR 2020

Switching Dynamical Systems with Deep Neural Networks

Similar lecture

LREN: Low-Rank Embedded Network for Sample-Free Hyperspectral Anomaly Detection