Italy

Deploying deep learning models with millions, even billions, of parameters is challenging given real world memory, power and compute constraints. In an effort to make these models more practical, in this paper, we propose a novel model compression approach that exploits linear dependence between the activations in a layer to eliminate entire structural units (neurons/convolutional filters). Our approach also adjusts the weights of the layer in a manner that is provably lossless while training if the removed neuron was perfectly predictable. We combine this approach with an annealing algorithm that may be applied during training, or even on a trained model, and demonstrate, using popular datasets, that our technique can reduce the parameters of VGG and AlexNet by more than 97% on CIFAR-10, 85% on Caltech-256, and 19% on ImageNet at less than 2% loss in accuracy. Furthermore, we provide theoretical results showing that in overparametrized, locally linear (ReLU) neural networks where redundant features exist, and with
correct hyperparameter selection, our method is indeed able to capture
and suppress those dependencies.


ICPR 2020

Exploiting Non-Linear Redundancy for Neural Model Compression

computer vision

deep learning

model compression

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

Recently, many multi-stream gaze estimation methods have been proposed. They estimate gaze from eye and face appearances and achieve reasonable accuracy. However, most of the methods simply concatenate the features extracted from eye and face appearance. The feature fusion process has been ignored. Thus, we propose a novel Adaptive Feature Fusion Network (AFF-Net), which performs gaze tracking task in mobile tablets. We stack two-eye feature maps and utilize Squeeze-and-Excitation layers to adaptively fuse two-eye features according to their similarity on appearance. Meanwhile, we also propose Adaptive Group Normalization to recalibrate eye features with the guidance of facial feature. Extensive experiments on both GazeCapture and MPIIFaceGaze datasets demonstrate consistently superior performance of the proposed method.


Adaptive Feature Fusion Network for Gaze Tracking in Mobile Tablets

Topological Data Analysis (TDA) is a rising field in machine learning. TDA seeks to extract information about the shape of a data set by examining its topological and geometric properties. Persistence diagrams, one of main tools in TDA, store topological information about the data. Persistence curves, a recently developed framework, provides a canonical and flexible way to encode the information presented in persistence diagrams into vectors. Based on persistence curves, the main contributions of this work are to (1) provide new sets of features for time series (2) propose a hybrid metric that takes both geometric and topological information of the time series into account, and (3) we apply these metrics to all the datasets in the UCR Time Series Classification Archive. Empirical results show that the proposed metrics perform better than the relevant benchmark in most cases.

A Hybrid Metric Based on Persistent Homology and Its Application to Signal Classification

In this work, we propose a novel method to generate compact image representations which uses local spatial information through an attention mechanism, named Generalized Local Attention Pooling (GLAP). This method, instead of being placed at the end layer of the backbone, is connected at an intermediate level, resulting in lower memory requirements. 
Experiments showcase that our proposed mechanism outperforms other pooling methods when compared with current state-of-the-art losses for deep metric learning.

Generalized Local Attention Pooling for Deep Metric Learning

This paper is to provide an overview of how object features from images influence face detection performance, and how to select synthetic faces to address specific features. To this end, we investigate the effects of occlusion, scale, viewpoint, background, and noise by using a novel synthetic image generator based on 3DU Face Dataset. To examine the effects of different features, we selected three detectors (Faster RCNN, HR, SSH) as representative of various face detection methodologies. Comparing different configurations of synthetic data on face detection systems, it showed that our synthetic dataset could complement face detectors to become more robust against features in the real world. Our analysis also demonstrated that a variety of data augmentation is necessary to address nuanced differences in performance.

Object features and face detection performance: Analyses with 3D-rendered synthetic data


A reliable detection of the R-peaks in an electrocardiogram
(ECG) is a fundamental step for further heart
rate variability (HRV) analysis, biometric recognition techniques
and additional ECG waveform based analysis. In this paper, a
novel real-time and low-complexity R-peak detection algorithm is
presented for single lead ECG signals. The detection algorithm
is divided in two stages. In the first pre-processing stage, the
QRS complex is enhanced by taking the double derivative,
squaring and moving window integration. In the second, the
detection of the R-peak is achieved based on a finite state machine
(FSM) approach. The detection threshold is dynamically adapted
and follows an exponential decay after each detection, making
it suitable for R-peak detection under fast heart rate (HR)
and R-wave amplitude changes without additional search back.
The proposed algorithm was evaluated in a private single lead
ECG database acquired using a FieldWiz wearable device. The
database comprises five recordings from four different subjects,
recorded during dynamic conditions, namely: running, trail
running and weightlifting. The raw ECG signals were annotated
for the R-peak and the proposed method benchmarked against
common QRS detectors. The combined acquisition setup and
presented approach resulted in R-peak detection Sensitivity (Se)
of 99.77% and Positive Predictive Value of (PPV) of 99.18%,
comparable to state of the art real time QRS detectors. Due to its
low computational complexity, this method can be implemented
in embedded wearable systems, suited for cardiovascular tracking
devices in dynamic use cases and R-peak detection.

A Low-Complexity R-peak Detection Algorithm with Adaptive Thresholding for Wearable Devices

The characteristics of training data is a fundamental consideration when constructing any supervised classifier. Class mislabelling and imbalance are major training data issues that often adversely affect machine learning algorithms, including ensembles. This work proposes extended bagging algorithms to better handle noisy and multi-class imbalanced classification tasks. These algorithms upgrade the sampling procedure by taking benefit of the confidence in ensemble classification outcome. The underlying idea is that a bagging ensemble learning algorithm can achieve greater performance if it is allowed to choose the data from which it learns. The effectiveness of the proposed methods is demonstrated in performing classification on 10 various data sets.

Boundary bagging to address training data issues in ensemble classification

In this paper we define a deep multi-stage architec-
ture for automated landmarking of craniomaxillofacial (CMF)
CT images. Our model is composed of three subnetworks that
first localize, on reduced-resolution images, areas where land-
marks may be found and then refine the search, at full-resolution
scale, through a hierarchical structure aiming at increasing the
granularity of the investigated region. The multi-stage pipeline is
designed to deal with full resolution data and does not require any
additional pre-processing step to reduce search space, as opposed
to existing methods that can be only adopted for searching
landmarks located in well-defined anatomical structures (e.g.,
mandibles). The automated landmarking system is tested on
identifying landmarks located in several CMF regions, achieving
an average error of 0.8 mm, significantly lower than expert
readings. The proposed model also outperforms baselines and
is on par with existing models that employ additional upstream
segmentation, on state-of-the-art benchmarks.


Deep Multi-stage Model for Automated Landmarking of Craniomaxillofacial Ct Scans


Estimating the Ratio of Edge-Users (REU) is an important issue in mobile networks, as it helps the subsequent adjustment of loads in different cells. However, existing approaches usually determine the REU manually, which are experience-dependent and labor-intensive, and thus the estimated REU might be imprecise. Considering the inherited graph structure of mobile networks, in this paper, we utilize a graph-based deep learning method for automatic REU estimation, where the practical cells are deemed as nodes and the load switchings among them constitute edges. Concretely, Graph Attention Network (GAT) is employed as the backbone of our method due to its impressive generalizability in dealing with networked data. Nevertheless, conventional GAT cannot make full use of the information in mobile networks, since it only incorporates node features to infer the pairwise importance and conduct graph convolutions, while the edge features that are actually critical in our problem are disregarded. To accommodate this issue, we propose an Edge-Aware Graph Attention Network (EAGAT), which is able to fuse the node features and edge features for REU estimation. Extensive experimental results on two real-world mobile network datasets demonstrate the superiority of our EAGAT approach to several state-of-the-art methods.

Edge-Aware Graph Attention Network for Ratio of Edge User Estimation in Mobile Networks

Learning graph convolutional networks (GCNs) is an emerging field which aims at generalizing convolutional operations to arbitrary non-regular domains. In particular, GCNs operating on spatial domains show superior performances compared to spectral ones, however their success is highly dependent on how the topology of input graphs is defined.
In this paper, we introduce a novel framework for graph convolutional networks that learns the topological properties of graphs. The design principle of our method is based on the optimization of a constrained objective function which learns not only the usual convolutional parameters in GCNs but also a transformation basis that conveys the most relevant topological relationships in these graphs. Experiments conducted on the challenging task of skeleton-based action recognition shows the superiority of the proposed method compared to handcrafted graph design as well as the related work.

Learning Connectivity with Graph Convolutional Networks

Current 3D object detection methods achieve accurate and efficient results on the standard point cloud dataset. However, in real-world applications, the point cloud samples obtained in the real-time running may be much sparser due to various reasons (occlusion, low reflectivity of objects and fewer laser beams) and existing methods do not consider the limitations of their models on sparse point clouds. To improve the robustness of an object detector to sparser point clouds, we propose PointDrop, which learns to drop the features of some key points in the point clouds to generate challenging sparse samples for data augmentation. Moreover, PointDrop is able to adjust the difficulty of the generated samples based on the capacity of the detector and thus progressively improve the performance of the detector. We create two sparse point clouds datasets from the KITTI dataset to evaluate our method, and the experimental results show that PointDrop significantly improves the robustness of the detector to sparse point clouds.

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Exploiting Non-Linear Redundancy for Neural Model Compression

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