Italy

Images, videos, audio signals, sensor data, can be easily collected in huge quantity by different devices and processed in order to emulate the human capability of elaborating a variety of different stimuli. Are multimodal signals useful to understand and anticipate human actions if acquired from the user viewpoint? This paper proposes to build an embedding space where inputs of different nature, but semantically correlated, are projected in a new representation space and properly exploited to anticipate the future user activity. 
To this purpose, we built a new multimodal dataset comprising video, audio, tri-axial acceleration, angular velocity, tri-axial magnetic field, pressure and temperature.
To benchmark the proposed multimodal anticipation challenge, we consider classic classifiers on top of deep learning methods used to build the embedding space representing multimodal signals.
The achieved results show that the exploitation of different modalities is useful to improve the anticipation of the future activity.

ICPR 2020

Anticipating Activity from Multimodal Signals

action anticipation

multimodal dataset

multimodal 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. 


Recent advances in sensing technologies require the design and development of pattern recognition models capable of processing spatiotemporal data efficiently. In this study, we propose a spatially and temporally aware tensor-based neural network for human pose classifiaction using three-dimensional skeleton data. Our model employs three novel components. First, an input layer capable of constructing highly discriminative spatiotemporal features. Second, a tensor fusion operation that produces compact yet rich representations of the data, and third, a tensor-based neural network that processes data representations in their original tensor form. Our model is end-to-end trainable and characterized by a small number of trainable parameters making it suitable for problems where the annotated data is limited. Experimental evaluation of the proposed model indicates that it can achieve state-of-the-art performance.


Space-Time Domain Tensor Neural Networks: An Application on Human Pose Classification

Many ML security, many paper study individual attacks, like for example evasion. In this presentation, we show why studying a single attack might be misleading in terms of robustness.

Killing four Birds with one Gaussian Process:
The relation between different Test-time attacks

In this work we introduce E-DNAS, a differentiable architecture search method, which improves the efficiency of NAS (Neural Architecture Search) methods in designing light-weight networks for the task of image classification. Concretely, E-DNAS computes, in a differentiable manner, the optimal size of a number of meta-kernels that capture patterns of the input data at different resolutions. We also leverage on the additive property of convolution operations to merge several kernels with different compatible sizes into a single one, reducing thus the number of operations and the time required to estimate the optimal configuration. We evaluate our approach on several datasets to perform classification. We report results in terms of the SoC (System on Chips) metric, typically used in the Texas Instruments TDA2x families for autonomous driving applications. The results show that our approach allows designing low latency architectures significantly faster than state-of-the-art

E-DNAS: Differentiable Neural Architecture Search for Embedded Systems

While in high demand for the development of deep learning approaches, extensive datasets of annotated unmanned aerial vehicle (UAV) imagery are still scarce today. Manual annotation, however, is time-consuming and, thus, has limited the potential for creating large-scale datasets. We tackle this challenge by presenting a procedure for the automatic creation of simulated UAV image sequences in urban areas and pixel-level annotations from publicly available data sources. We synthesize photo-realistic UAV imagery from GOOGLE EARTH STUDIO and derive annotations from an open CityGML model that not only provides geometric but also semantic information.

Derivation of Geometrically and Semantically
Annotated UAV Datasets at Large Scales from 3D
City Models

In recent years, multi-view multi-task clustering has received much attention. 
There are several real-life problems that involve both multi-view as well as multi-task clustering, i.e., the tasks are closely related, and each task can be analyzed using multiple views. Traditional multi-task multi-view clustering algorithms utilize single-objective optimization based approaches and cannot apply too-many regularization terms. However, these problems are inherently some multi-objective optimization problems because conflict may be between different views within a given task and also between different tasks, necessitating a trade-off. Based on these observations, in this paper, we have proposed a novel multi-task multi-view multi-objective optimization (MTMV-MO) based clustering algorithm which simultaneously optimizes three objectives, i.e., within-view task relation, within-task view relation and the quality of the clusters obtained. The proposed methodology (MTMV-MO) is evaluated on four different datasets and the results are compared with five state-of-the-art algorithms in terms of Adjusted Rand Index (ARI) and Classification Accuracy (%CoA). MTMV-MO illustrates an improvement of 1.5-2% in terms of ARI and 4-5% in terms of %CoA compared to the state-of-the-art algorithms.

Multi-task Multi-view Clustering using Multi-objective Optimization

The clustering of unlabeled raw images is a daunting task, which has recently been approached with some success by deep learning methods. Here we propose an unsupervised clustering framework, which learns a deep neural network in an end-to-end fashion, providing direct cluster assignments of images without additional processing. Multi-Modal Deep Clustering (MMDC), trains a deep network to align its image embeddings with target points sampled from a Gaussian Mixture Model distribution. The cluster assignments are then determined by mixture component association of image embeddings. Simultaneously, the same deep network is trained to solve an additional self-supervised task of predicting image rotations. This pushes the network to learn more meaningful image representations that facilitate a better clustering. Experimental results show that MMDC achieves or exceeds state-of-the-art performance on six challenging benchmarks. On natural image datasets we improve on previous results with significant margins of up to 20% absolute accuracy points, yielding an accuracy of 82% on CIFAR-10, 45% on CIFAR-100 and 69% on STL-10.

Multi-Modal Deep Clustering: Unsupervised Partitioning of Images

I present my work on Temporal Collaborative Filtering with applications on Recommender Systems. For this method, I employ a combination of GNNs and RNNs solving the sparsity problem in the process. Improvement in performance is observed over graph, non-graph, temporal and non-temporal state-of-the-art CF methods.

Temporal Collaborative Filtering with Graph Convolutional Neural Networks

As the specialty of hyperspectral image (HSI), it consists of 2D spatial and 1D spectral information. In the field of deep learning, HSI classification is an appealing research topic. Many existing methods process the HSI in spatial or spectral domain separately, which cannot fully extract the representative features, and the most used 3D convolutional neural network (3D-CNN) will suffer from mixing up complex spectral information. In this paper, we propose a spatial-spectral unified method by
using recurrent neural networks (RNN) and multi-scanning direction strategy to construct spatial-spectral information sequences for learning the spatial dependencies among the central pixel and neighboring pixels.

Multi-Scanning Based Recurrent Neural Network for Hyperspectral Image Classification


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.

Prior Knowledge about Attributes Learning a More Effective Potential Space for Zero-Shot Recognition

Accurate extrinsic calibration of wide baseline multi-camera systems enables better understanding of 3D scenes for many applications and is of great practical importance. Classical Structure-from-Motion calibration methods require special calibration equipment so that accurate point correspondences can be detected between different views. In addition, an operator with some training is usually needed to ensure that data is collected in a way that leads to good calibration accuracy. This limits the ease of adoption of such technologies. Recently, methods have been proposed to use human pose estimation models to establish point correspondences, thus removing the need for any special equipment. The challenge with this approach is that human pose estimation algorithms typically produce much less accurate feature points compared to classical patch-based methods. Another problem is that ambient human motion might not be optimal for calibration. We build upon prior works and introduce several novel ideas to improve the accuracy of human-pose-based extrinsic calibration. Our first contribution is a robust reprojection loss based on a better understanding of the sources of pose estimation error. Our second contribution is a 3D human pose likelihood model learned from motion capture data. We demonstrate significant improvements in calibration accuracy by evaluating our method on four publicly available datasets.

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Space-Time Domain Tensor Neural Networks: An Application on Human Pose Classification

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