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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&#39;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.

ICPR 2020

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

lidar

spiking neural networks

object recognition

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NAML 2021

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.

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. 


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.

Partial Monotone Dependence

Recently, action recognition methods using graph convolutional networks (GCN) have shown remarkable performance thanks to its concise but effective representation of human body motion. Prior methods construct human body motion graph building edges between neighbor or distant body joints. On the other hand, human action contains lots of temporal variations showing strong temporal correlations between joint motions. Thus the characterization of action requires a comprehensive analysis of joint motion correlations on spatial and temporal domains. In this paper, we propose Joint-temporal Motion Graph Convolutional Networks (JT-MGCN) in which joint-temporal edges learn the correlations between different joints at different time. Experimental evaluation on large public data sets such as NTU rgb+d data set and kinetics-skeleton data set show outstanding action recognition performance.

JT-MGCN: Joint-temporal Motion Graph Convolutional Network for Skeleton-Based Action Recognition


Dealing with opioid addiction and its long-term
consequences is of great importance, as the addiction to opioids is
emerged gradually, and established strongly in a given patient’s
body. Based on recent research, quitting the opioid requires
clinicians to arrange a gradual plan for the patients who deal
with the difficulties of overcoming addiction. This, in turn,
necessitates observing the patients’ wellness periodically, which
is conventionally made by setting clinical appointments. With
the advent of wearable sensors continuous patient monitoring
becomes possible. However, the data collected through the sensors
is pervasively noisy, where using sensors with different sampling
frequency challenges the data processing. In this work, we handle
this problem by using data from cognitive tests, along with heart
rate (HR) and heart rate variability (HRV). The proposed recipe
enables us to interpret the data as a feature space, where we can
predict the wellness of the opioid patients by employing extreme
gradient boosting (XGBoost), which results in 96.12% average
accuracy of prediction as the best-achieved performance.

XGBoost to Interpret the Opioid Patients’ State Based on Cognitive and Physiological Measures

Video Prediction is a challenging but interesting task of predicting future frames from a given set context frames that belong to a video sequence. Video prediction models have prospective applications in maneuver planning, healthcare, autonomous navigation and simulation. One of the major challenges in future frame generation is the high dimensional nature of visual data. To handle this, we propose a Mutual Information Predictive Auto-Encoder (MIPAE) framework that reduces the task of predicting high dimensional video frames by factorising video representations into content and low dimensional pose latent variables. Our approach leverages the temporal structure in the latent generative factors of video sequences by applying a novel mutual information loss to learn disentangled video representations. A standard LSTM network is used to predict these low dimensional pose representations. Content and the predicted pose representations are decoded to generate future frames. We also propose a metric based on mutual information gap (MIG) to quantitatively access the effectiveness of disentanglement on DSprites and MPI3D-real datasets. MIG scores corroborate the visual superiority of frames
predicted by MIPAE. We also compare our method quantitatively on LPIPS, SSIM and PSNR evaluation metrics.

Mutual Information based Method for Unsupervised Disentanglement of Video Representation


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.

Energy Minimum Regularization in Continual Learning

Facial expression recognition is inherently a challenging task, especially for the in-the-wild images with various occlusions and large pose variations, which may lead to the loss of some crucial information. To address it, in this paper, we propose
an attentive hybrid architecture (AHA) which learns global, local and integrated features based on different face regions. Compared with one type of feature, our extracted features own complementary information and can reduce the loss of crucial information. Specifically, AHA contains three branches, where all sub-networks in those branches employ the attention mechanism to further localize the interested pixels/regions. Moreover, we propose a two-step fusion strategy based on LSTM to deeply explore the hidden correlations among different face regions.
Extensive experiments on four popular expression databases (i.e., CK+, FER-2013, SFEW 2.0, RAF-DB) show the effectiveness of the proposed method.

Attentive Hybrid Feature with Two-Step Fusion forFacial Expression Recognition

Spiking neural networks (SNNs) receive widespread attention because of their low-power hardware characteristic and brain-like signal response mechanism, but currently, the performance of SNNs is still behind Artificial Neural Networks
(ANNs). We build an information theory-inspired system called Stochastic Probability Adjustment (SPA) system to reduce this gap. The SPA maps the synapses and neurons of SNNs into a probability space where a neuron and all connected pre-synapses are represented by a cluster. The movement of synaptic transmitter between different clusters is modeled as a Brownianlike stochastic process in which the transmitter distribution is adaptive at different firing phases. We experimented with a wide range of existing unsupervised SNN architectures and achieved consistent performance improvements. The improvements in classification accuracy have reached 1.99% and 6.29% on the MNIST and EMNIST datasets respectively.

SPA: Stochastic Probability Adjustment for System Balance of Unsupervised SNNs


Medical image segmentation is a critical field in the
domain of computer vision and with the growing acclaim of deep
learning based models, research in this field is constantly expanding.
Optical coherence tomography (OCT) is a non-invasive
method that scans the human’s retina with depth. It has been
hypothesized that the thickness of the retinal layers extracted
from OCTs could be an efficient and effective biomarker for
early diagnosis of AD. In this work, we aim to design a selftraining
model architecture for the task of segmenting the retinal
layers in OCT scans. Neural architecture search (NAS) is a
subfield of AutoML domain, which has a significant impact on
improving the accuracy of machine vision tasks. We integrate
the NAS algorithm with a Unet auto-encoder architecture as
its backbone. Then, we employ our proposed model to segment
the retinal nerve fiber layer in our preprocessed OCT images
with the aim of AD diagnosis. In this work, we trained a
super-resolution generative adversarial network on the raw OCT
scans to improve the quality of the images before the modeling
stage. In our architecture search strategy, different primitive
operations suggested to find down- & up-sampling Unet cell
blocks and the binary gate method has been applied to make
the search strategy more practical. Our architecture search
method is empirically evaluated by training on the Unet and
NAS-Unet from scratch. Specifically, the proposed NAS-Unet
training significantly outperforms the baseline human-designed
architecture by achieving 95.1% in the mean Intersection over
Union metric and 79.1% in the Dice similarity coefficient.

OCT Image Segmentation Using Neural Architecture Search and SRGAN

Remote sensing image retrieval (RSIR) is the process of ranking database images depending on the degree of similarity compared to the query image. As the complexity of RSIR increases due to the diversity in shooting range, angle, and location of remote sensors, there is an increasing demand for methods to address these issues and improve retrieval performance. In this work, we introduce a novel method for retrieving aerial images by merging group convolution with attention mechanism and metric learning, resulting in robustness to rotational variations.

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