Italy

Domain adaptation has emerged as a crucial technique to address the problem of domain shift, which exists when applying an existing model to a new population of data. Adversarial learning has made impressive progress in learning a domain invariant representation via building bridges between two domains. However, existing adversarial learning methods tend to only employ a domain discriminator or generate adversarial examples that affect the original domain distribution. Moreover, little work has considered confident continuous learning using an existing source classifier for domain adaptation. In this paper, we develop adversarial continuous learning in a unified deep architecture. We also propose a novel correlated loss to minimize the discrepancy between the source and target domain. Our model increases robustness by incorporating high-confidence samples from the target domain. The transfer loss jointly considers the original source image and transfer examples in the target domain. Extensive experiments demonstrate significant improvements in classification accuracy over the state of the art.

ICPR 2020

Adversarial Continuous Learning in Unsupervised Domain Adaptation

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. 


The dominant paradigm in spatiotemporal action detection is to classify actions using spatiotemporal features learned by 2D or 3D Convolutional Networks. We argue that several actions are characterized by their context, such as relevant objects and actors present in the video. To this end, we introduce an architecture based on self-attention and Graph Convolutional Networks in order to model contextual cues, such as actor-actor and actor-object interactions, to improve human action detection in video. We are interested in achieving this in a weakly-supervised setting, i.e. using as less annotations as possible in terms of action bounding boxes. Our model aids explainability by visualizing the learned context as an attention map, even for actions and objects unseen during training. We evaluate how well our model highlights the relevant context by introducing a quantitative metric based on recall of objects retrieved by attention maps. Our model relies on a 3D convolutional RGB stream, and does not require expensive optical flow computation. We evaluate our models on the DALY dataset, which consists of human-object interaction actions. Experimental results show that our contextualized approach outperforms a baseline action detection approach by more than 2 points in Video-mAP.

Spot What Matters: Learning Context Using Graph Convolutional Networks for Weakly-Supervised Action Detection

The performance of Convolutional Neural Networks for image classification has vastly and steadily increased during the last years. This success goes hand in hand with the need to explain and understand their decisions: opening the black box. The problem of attribution specifically deals with the characterization of the response of Convolutional Neural Networks by identifying the input features responsible for the model's decision. Among all attribution methods, perturbation-based methods are an important family based on measuring the effect of perturbations applied to the input image in the model's output. In this paper, we discuss the limitations of existing approaches and propose a novel perturbation-based attribution method guided by semantic segmentation. Our method inhibits specific image areas according to their assigned semantic label. Hereby, perturbations are link up with a semantic meaning and a complete attribution map is obtained for all image pixels. In addition, we propose a particularization of the proposed method to the scene recognition task which, differently than image classification, requires multi-focus attribution models. The proposed semantic-guided attribution method enables us to delve deeper into scene recognition interpretability by obtaining for each scene class the sets of relevant, irrelevant and distracting semantic labels.

AIVisualizing the Effect of Semantic Classes in the Attribution of Scene Recognition Models

COVID-19 pandemic has adversely and disproportionatelyimpacted people suffering from mental health issues and substance useproblems. This has been exacerbated by social isolation during the pan-demic and the social stigma associated with mental health and sub-stance use disorders, making people reluctant to share their struggles andseek help. Due to the anonymity and privacy they provide, social mediaemerged as a convenient medium for people to share their experiencesabout their day to day struggles. Reddit is a well-recognized social mediaplatform that provides focused and structured forums called subreddits,that users subscribe to and discuss their experiences with others. Tem-poral assessment of the topical correlation between social media post-ings about mental health/substance use and postings about Coronavirusis crucial to better understand public sentiment on the pandemic andits evolving impact, especially related to vulnerable populations. In thisstudy, we conduct a longitudinal topical analysis of postings between sub-reddits r/depression, r/Anxiety, r/SuicideWatch, and r/Coronavirus, andpostings between subreddits r/opiates, r/OpiatesRecovery, r/addiction,and r/Coronavirus from January 2020 - October 2020. Our results show ahigh topical correlation between postings in r/depression and r/Coronavi-rus in September 2020. Further, the topical correlation between postingson substance use disorders and Coronavirus fluctuates, showing the high-est correlation in August 2020. By monitoring these trends from plat-forms such as Reddit, epidemiologists, and mental health professionalscan gain insights into the challenges faced by communities for targetedinterventions.

COVID-19 and Mental Health/Substance Use Disorders on Reddit: A Longitudinal Study

The objective of this study is to evaluate and compare the performance of a set of low-cost multi-modal head tracking systems incorporating facial gestures as a switching mechanism. The proposed systems are aimed to enable severely disabled patients to access a computer. In this paper, we are comparing RGB (2D) and RGB-D (3D) sensors for both head tracking and facial gesture recognition. System evaluations and usability assessment were carried out on 21 healthy individuals. Two
types of head tracking systems were compared - a web camera-based and another using the Kinect sensor. The two facial switching mechanisms were eye blink and eyebrows movement. Fitts' Test is used to evaluate the proposed systems. Movement Time (MT) was used to rank the performance of the proposed systems. The Kinect-Eyebrows system had the lowest MT, followed by the Kinect-Blink, Webcam-Blink and Webcam-Eyebrows systems. The 3D Kinect systems performed better than the 2D Vision systems for both gestures. Both Kinect systems have the lowest MT and best performance, thus showing the advantage of using depth.

Development and evaluation of a mouse emulator using multi-modal real-time headtracking systems with facial gesture recognition as a switching mechanism

THDL system include THDL-Det sub-system for text detection and THDL-Rec sub-system for text recognition. The THDL-Det sub-system is an end-to-end text spotter based on the Mask R-CNN instance segmentation framework. The THDL-Rec sub-system adopts a CNN-LSTM-CTC framework to recognize Arabic text lines in videos.

TH-DL system for AcTiV2020 Competition

Deep image clustering approaches typically use autoencoder
architectures to learn compressed latent representations suitable for clustering tasks. However, they do not effectively regulate the latent space
during training, leading to low performance and diminished applicability
to different datasets. In this paper, we propose a deep clustering model
combining maximum mean discrepancy (MMD) regularization and self-learning clustering optimization to mitigate this problem. Specifically,
we first train the network to improve its image reconstruction ability
by minimizing both reconstruction loss and MMD divergence from a
target distribution. Then, the model gradually learns from its own high-confidence predictions to further optimize the latent distribution. We
validate the network’s performance on different benchmark image sets
using standard clustering metrics, without changing network configuration or adjusting hyper-parameters between datasets. The proposed
model provides top clustering performance across datasets while being
more robust than state-of-the-art methods.

Deep Image Clustering Using Self-Learning Optimization in a Variational Auto-Encoder

To facilitate deep learning based table extraction from unstructured digital files, we disclose a standard Chinese financial dataset from PDF files for table extraction benchmark test, which is diverse, sufficient and comprehensive. We also propose a GCN-based algorithm GFTE as a baseline with a novel idea of integrating all possible types of ground truth together.

GFTE: Graph-based Financial Table Extraction

Relative attributes indicate the strength of a particular attribute between image pairs. We introduce a deep Siamese network with rank SVM loss function, called Deep-RankSVM, that can decide which one of a pair of images has a stronger presence of a specific attribute. The network is trained in an end-to-end fashion to jointly learn the visual features and the ranking function. The trained network for an attribute can predict the relative strength of that attribute in novel images. We demonstrate the effectiveness of our approach against the state-of-the-art methods on four image benchmark datasets: LFW-10, PubFig, UTZap50K-2 and UTZap50K-lexi datasets. Deep-RankSVM surpasses state-of-art in terms of the average accuracy across attributes, on three of the four image benchmark datasets.

Relative Attribute Classification with Deep-RankSVM

In this paper, we propose the research of the recursive division approach to get an explanation for a particular decision of the shallow black box model. The core of the proposed method is the division of the image being classified into separate rectangular parts, followed by the analysis of their influence on the classification result. Such divisions are repeated recursively until the explanation of the classification result is found, or the size of parts is too small. As a result, the pair of images with complement hidden parts is discovered, the first one of which preserves both the most valuable parts and the classification result of the initial image. The second image represents the result of hiding the most valuable parts of an initial image that leads to the different classification for the binary classification problem. Experimental research (applied for Food-5K and concrete crack images datasets) proved that the quality of the proposed method might be close to LIME or even better, while the performance of recursive division is better.

Recursive Division of Image for Explanation of Shallow CNN Models

Deep Neural Networks are becoming the prominet solution
when using machine learning models. However, they suffer of a black-
box effect that renders complicated their inner workings interpretation
and thus the understanding of their successes and failures. Information
visualisation is one way among others to help in their interpretability
and hypothesis deduction. This paper presents a novel way to visualize
a trained DNN in order to depict at the same time its architecture
and its way of treating the classes of a test dataset at the layer level.
This way, it is possible to visually detect where the DNN starts to be
able to discriminate the classes or where it could decrease its separation
ability (and thus detect an oversized network). We have implemented the
approach and validated it using several well-known datasets and networks.
Results show the approach is promising and deserves further studies.

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