Italy

Data distributions in streaming environments are usually not stationary. In order to maintain a high predictive quality at all times, online learning models need to adapt to distributional changes, which are known as concept drift. The timely and robust identification of concept drift can be difficult, as we never have access to the true distribution of streaming data. In this work, we propose a novel framework for the detection of real concept drift, called ERICS. By treating the parameters of a predictive model as random variables, we show that concept drift corresponds to a change in the distribution of optimal parameters. To this end, we adopt common measures from information theory. The proposed framework is completely model-agnostic. By choosing an appropriate base model, ERICS is also capable to detect concept drift at the input level, which is a significant advantage over existing approaches. An evaluation on several synthetic and real-world data sets suggests that the proposed framework identifies concept drift more effectively and precisely than various existing works.

ICPR 2020

Learning Parameter Distributions to Detect Concept Drift in Data Streams

concept drift detection

data streams

online learning

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.

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. 


Facial Expression Recognition (FER) is a challenging
task because many factors of variation such as pose,
illumination and identity-specific attributes are entangled with
the expression information in an expressive face image. Recent
works show that the performance of a FER algorithm
can be improved by disentangling the expression information
from identity features. In this paper, we present Transferbased
Expression Recognition Generative Adversarial Network
(TER-GAN) that combines the effectiveness of a novel feature
disentanglement technique with the concept of identity-invariant
expression representation learning for FER. More specifically,
TER-GAN learns a disentangled expression representation by
extracting expression features from one image and transferring
the expression information to the identity of another image. To
improve the feature disentanglement process and to learn an
identity-invariant expression representation, we introduce a novel
expression consistency loss and an identity consistency loss that
exploit expression and identity information from both real and
synthetic images. We evaluated the performance of our proposed
facial expression recognition technique by employing four public
facial expression databases, CK+, Oulu-CASIA, MMI, BU-3DFE.
The experimental results show the effectiveness of the proposed
technique.

Facial Expression Recognition By Using a Disentangled Identity-Invariant Expression
Representation

Vision is the first indicator for coaches to assess the quality of a sport gesture. However, gesture analysis using computer vision is often restricted to laboratory experiments, far from the real conditions in which athletes train on a daily basis. In this perspective, we introduce 3D ball trajectories analysis using a single camera with very few acquisition constraints. A key point of the proposal is the estimation of the apparent ball size for obtaining ball to camera distance. For this purpose, a 2D CNN is trained using a generated dataset that enables a reliable ball size extraction, even in case of high motion blur. The final objective is not only to be able to determine ball trajectories, but most importantly to retrieve their relevant physical parameters. Those kinematics parameters could help coaches and players to better analyze strokes with strong rotation effects during training sessions or matches. With a precise estimation of those trajectories, it is indeed possible to extract the ball tangential and rotation speed, related to the so-called Magnus effect. Validation experiments for characterizing table tennis strokes are presented on both a synthetic dataset and on real video sequences.

Extraction and analysis of 3D kinematic parameters of Table Tennis ball from a single camera

Video object segmentation(VOS) has been a fundamental topic in recent years, and many deep learning-based methods have achieved state-of-the-art performance on multiple benchmarks. However, most of these methods rely on pixel-level matching between the template and the searched frames on the whole image while the targets only occupy a small region. Calculating on the entire image brings lots of additional computation cost. Besides, the whole image may contain some distracting information resulting in many false-positive matching points. We propose an efficient siamese dynamic mask estimation network for fast video object 
segmentation. The VOS is decoupled into two tasks, i.e., mask feature learning
and dynamic kernel prediction.

Siamese Dynamic Mask Estimation Network for Fast Video Object Segmentation

State-of-the-art, high capacity deep neural networks not only require large amounts of labelled training data, they are also highly susceptible to labelling errors in this data, typically resulting in large efforts and costs and therefore limiting the applicability of deep learning. To alleviate this issue, we propose a novel meta training and labelling scheme that is able to use inexpensive unlabelled data by taking advantage of the generalization power of deep neural networks. We show experimentally that by solely relying on one network architecture and our proposed scheme of combining self-training with pseudolabels, both label quality and resulting model accuracy, can be improved significantly. Our method achieves state-of-the-art results, while being architecture agnostic and therefore broadly applicable. Compared to other methods dealing with erroneous labels, our approach does neither require another network to be trained, nor does it necessarily need an additional, highly accurate reference label set. Instead of removing samples from a labelled set, our technique uses additional sensor data without the need for manual labelling. Furthermore, our approach can be used for semi-supervised learning.

Iterative Label Improvement: Robust Training by Confidence Based Filtering and Dataset Partitioning


Localizing page elements/objects such as tables, figures, equations, etc. is the primary step in extracting information from document images. We propose a novel end-to-end trainable deep network, (CDeC-Net) for detecting tables present in the documents. The proposed network consists of a multistage extension of Mask R-CNN with a dual backbone having deformable convolution for detecting tables varying in scale with high detection accuracy at higher IoU threshold. We empirically evaluate CDeC-Net on the publicly available benchmark datasets with extensive experiments.
Our solution has three important properties: (i) a single trained model CDeC-Net that performs well across all the popular benchmark datasets; (ii) we report excellent performances across multiple, including higher, thresholds of IoU; (iii) by following the same protocol of the recent papers for each of the benchmarks, we consistently demonstrate the superior quantitative performance. Our code and models are publicly available at https://github.com/mdv3101/CDeCNet for enabling reproducibility of the results.

CDeC-Net: Composite Deformable Cascade Network for Table Detection in Document Images


We propose a novel scheme for improving the word recognition accuracy using word image embeddings. We use a trained text recognizer, which can predict multiple text hypothesis for a given word image. Our fusion scheme improves the recognition process by utilizing the word image and text embeddings obtained from a trained word image embedding network. We propose EmbedNet, which is trained using a triplet loss for learning a suitable embedding space where the embedding of the word image lies closer to the embedding of the corresponding text transcription. The updated embedding space thus helps in choosing the correct prediction with higher confidence. To further improve the accuracy, we propose a plug-and-play module called Confidence based Accuracy Booster (CAB). The CAB module takes in the confidence scores obtained from the text recognizer and Euclidean distances between the embeddings to generate an updated distance vector. The updated distance vector has lower distance values for the correct words and higher distance values for the incorrect words. We rigorously evaluate our proposed method systematically on a collection of books in the Hindi language. Our method achieves an absolute improvement of around 10% in terms of word recognition accuracy.

Improving Word Recognition using Multiple Hypotheses and Deep Embeddings


Recently, with the increasing number of large-scale remote sensing images, the demand for large-scale remote sensing image object classification is growing and attracting the interest of many researchers. Hashing, because of its low memory requirements and high time efficiency, has been widely solve the problem of large-scale remote sensing image. Supervised hashing methods mainly leverage the label information of remote sensing image to learn hash function, however, the similarity of the original feature space cannot be well preserved, which can not meet the accurate requirements for object classification of remote sensing image. To solve the mentioned problem, we propose a novel method named Optimized Projection Supervised Discrete Hashing(OPSDH), which jointly learns a discrete binary codes generation and optimized projection constraint model. It uses an effective optimized projection method to further constraint the supervised hash learning and generated hash codes preserve the similarity based on the data label while retaining the similarity of the original feature space. The experimental results show that OPSDH reaches improved performance compared with the existing hash learning methods and demonstrate that the proposed method is more efficient for operational applications.

Object Classification of Remote Sensing Images Based on Optimized Projection Supervised Discrete Hashing


In this paper we introduce a novel public dataset for developing and benchmarking indoor localization systems. We have selected and 3D mapped a set of representative indoor environments including a large office building, a conference room, a workshop, an exhibition area and a restaurant. Our acquisition pipeline is based on a portable LiDAR SLAM backpack to map the buildings and to accurately track the pose of the user as it moves freely inside them. We introduce the calibration procedures that enable us to acquire and geo-reference live data coming from different independent sensors rigidly attached to the backpack. This has allowed us to collect long sequences of spherical and stereo images, together with all the sensor readings coming from a consumer smartphone and locate them inside the map with centimetre accuracy. The dataset addresses many of the limitations of existing indoor localization datasets regarding the scale and diversity of the mapped buildings; the number of acquired sequences under varying conditions; the accuracy of the ground-truth trajectory; the availability of a detailed 3D model and the availability of different sensor types. It enables the benchmarking of existing and the development of new indoor localization approaches, in particular for deep learning based systems that require large amounts of labeled training data.

RISEdb: a Novel Indoor Localization Dataset


A poster of a robust text recognizer for irregular text.

IBN-STR: A Robust Text Recognizer for Irregular Text in Natural Scenes


This paper presents a parametric variational autoencoder-based human target detection and localization framework working directly with the raw analog-to-digital converter data from the frequency modulated continuous wave radar. We propose a parametrically constrained variational autoencoder, with residual and skip connections, capable of generating the clustered and localized target detections on the range-angle image. Furthermore, to circumvent the problem of training the proposed neural network on all possible scenarios using real radar data, we propose domain adaptation strategies whereby we first train the neural network using ray tracing based model data and then adapt the network to work on real sensor data. This strategy ensures better generalization and scalability of the proposed neural network even though it is trained with limited radar data. We demonstrate the superior detection and localization performance of our proposed solution compared to the conventional signal processing pipeline and earlier state-of-art deep U-Net architecture with range-doppler images as inputs.


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