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ICPR 2020

Machine Learning for Storage Location Prediction in Industrial High Bay Warehouses

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The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
AAAI is providing you with a conference planner, which you can use to help organize your itinerary of activities. This includes talks to attend in person, talks to attend remotely, breaks with colleagues and your site seeing activities. To access this conference planner, please go to [https://aaai-2023.takemobi.io](https://aaai-2023.takemobi.io).

In order to access this site, you need to register. If you haven't already, please register [here](https://aaai.org/Conferences/AAAI-23/registration/).


AAAI 2023

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

technical paper

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. 


Multi-Input ConvLSTM

RADARSAT-2 Synthetic-Aperture Radar Land Cover Segmentation using Deep Convolutional Neural Networks

Analysis of the Scalability of a Deep-Learning Network for Steganography "Into the Wild"

Despite the exceptional success in face recognition related technologies, handling large pose variations still remains a key challenge. Current techniques for pose-robust face recognition either, directly extract pose-invariant features, or first synthesize a face that matches the target pose before feature extraction. It is more desirable to learn face representations equivariant to pose variations. To this end, this paper proposes a deep meta Capsule network-based Equivariant Embedding Model (DM-CEEM) with three distinct novelties. First, the proposed RB-CapsNet allows DM-CEEM to learn an equiv- ariant embedding for pose variations and achieve the desired transformation for input face images. Second, we introduce a new version of a Capsule network called RB-CapsNet to extend CapsNet to perform a profile-to-frontal face transformation in deep feature space. Third, we train the DM-CEEM in a meta way by treating a single overall classification target as multiple sub-tasks that satisfy certain unknown probabilities. In each sub-task, we sample the support and query sets randomly. The experimental results on both controlled and in-the-wild databases demonstrate the superiority of DM-CEEM over state-of-the-art.

Pose-robust Face Recognition by Deep Meta Capsule network-based Equivariant Embedding

Integrated Gradients as an attribution method for deep neural network models offers simple implementability. However, it suffers from noisiness of explanations which affects the ease of interpretability. The SmoothGrad technique is proposed to solve the noisiness issue and smoothen the attribution maps of any gradient-based attribution method. In this paper, we present SmoothTaylor as a novel theoretical concept bridging Integrated Gradients and SmoothGrad, from the Taylor’s theorem perspective. We apply the methods to the image classification problem, using the ILSVRC2012 ImageNet object recognition dataset, and a couple of pretrained image models to generate attribution maps. These attribution maps are empirically evaluated using quantitative measures for sensitivity and noise level. We further propose adaptive noising to optimize for the noise scale hyperparameter value. From our experiments, we find that the SmoothTaylor approach together with adaptive noising is able to generate better quality saliency maps with lesser noise and higher sensitivity to the relevant points in the input space as compared to Integrated Gradients.

Understanding Integrated Gradients with SmoothTaylor for Deep Neural Network Attribution

Eye tracking systems that estimate the point-of-gaze are essential in extended reality (XR) systems as they enable new interaction paradigms and technological improvements. It is important for these systems to maintain accuracy when the headset moves relative to the head (known as device slippage) due to head movements or user adjustment. One of the most accurate eye tracking techniques, which is also insensitive to shifts of the system relative to the head, uses two or more infrared (IR) light emitting diodes to illuminate the eye and an IR camera to capture images of the eye. 
An essential step in estimating the point-of-gaze in these systems is the precise determination of the location of two or more corneal reflections (virtual images of the IR-LEDs that illuminate the eye) in images of the eye. Eye trackers tend to have multiple light sources to ensure at least one pair of reflections for each gaze position. The use of multiple light sources introduces a difficult problem: the need to match the corneal reflections with the corresponding light source over the range of expected eye movements. 
Corneal reflection detection and matching often fail in XR systems due to the proximity of camera and steep illumination angles of light sources with respect to the eye. The failures are caused by corneal reflections having varying shape and intensity levels or disappearance due to rotation of the eye, or the presence of spurious reflections.
We have developed a fully convolutional neural network, based on the UNET architecture, that solves the detection and matching problem in the presence of spurious and missing reflections. Eye images of 25 people were collected in a virtual reality headset using a binocular eye tracking module consisting of five infrared light sources per eye. A set of 4,000 eye images were manually labelled for each of the corneal reflections, and data augmentation was used to generate a dataset of 40,000 images. The network is able to correctly identify and match 91% of corneal reflections present in the test set. This is comparable to a state-of-the-art deep learning system, but our approach requires 33 times less memory and executes 10 times faster. The proposed algorithm, when used in an eye tracker in a VR system, achieved an average mean absolute gaze error of 1°. This is a significant improvement over the state-of-the-art learning-based XR eye tracking systems that have reported gaze errors of 2-3°.


Detection and Correspondence Matching of Corneal Reflections for Eye Tracking Using Deep Learning

Real-time Cross-dataset Quality Production Assessment in Industrial Laser Cutting Machines

SURVANT: an innovative semantics-based Surveillance Video Archives Investigation Assistant

Assessement of CNN-Based Methods for Poverty Estimation from Satellite images


This paper presents chewing data analysis based on the new approach of chewing detection for diet monitoring applications. The proposed approach is based on chewing detection using a proximity sensor in capturing the temporalis muscle movement during chewing. The aim is to support the development of non-contact-based chewing detection. A wearable device of eye-glass is used with the sensor mounted at the right side of the eyeglass temple using a 3D printed housing. The main activity involved in this study is resting and eating, three test food that represents different food hardness (carrot, banana, and apple) with a portion of one spoonful. Several upper cut-off frequencies (fc2) of bandpass filters were used during the analyses. The signals were evaluated using accuracy and F1-score for classification and the absolute mean of error for chewing count estimation. In the classification stage, using the setting of a 10-fold cross-validation method and a 3s segmented window, fc2 of 6Hz gives the highest accuracy with 97.6%, while, 2.5Hz gives the lowest accuracy of 92.6%. However, in the chewing count estimation stage, which is based on a 240s segmented window, 2.4Hz able to give a smaller percentage absolute error of 2.69%, compare to 6Hz with 12.11%. It can be concluded that the chewing frequency was under 2.5Hz, but, the self-reporting labeling approach used in this study reduced the accuracy of the system as fc2 equal to 2.5Hz is used. Further analysis of chewing count shows that might in relating the total chewing count with different food hardness.

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Machine Learning for Storage Location Prediction in Industrial High Bay Warehouses

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