Italy


Image2latex usually means converts mathematical formulas in images into latex markup. It is a very challenging job due to the complex two-dimensional structure, variant scales of input, and very long representation sequence. In order to make full use of the structure and position information of the formula, we propose a global context-based network with transformer.

ICPR 2020

Global Context-Based Network with Transformer for Image2latex

transormer

formula recognition

encoder-decoder

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

In this paper, we propose a new deep neural network system, called Yolo+FPN, which fuses both 2D and 3D object detection algorithms to achieve better real-time object detection results and faster inference speed, to be used on real robots. Finding an optimized fusion strategy to efficiently combine 3D object detection with 2D detection information is useful and challenging for both indoor and outdoor robots. In order to satisfy real-time requirements, a trade-off between accuracy and efficiency is needed. We not only have improved training and test accuracies and lower mean losses on the KITTI object detection benchmark, but also achieve competitive average precision on 3D detection of all classes in three levels of difficulty comparing with other state-of-the-art methods. Also, we implemented Yolo+FPN system using an RGB-D camera, and compared the speed of object detection using different GPUs. For the real implementation of both indoor and outdoor scenes, we focus on person detection, which is the most challenging and important among the three classes.

Yolo+FPN: 2D and 3D Fused Object Detection with an RGB-D Camera


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.

Signature features with the visibility transformation


The detection of baselines of text is a necessary preprocessing step for many modern methods of automatic handwriting recognition. In this work, we present a two-stage system for the automatic detection of text baselines of handwritten text. In a first step, we perform pixel-wise segmentation on the document image to classify pixels as baselines, start points, end points and background. This segmentation is then used to extract the start points of lines. Starting from these points we extract the baseline using a recurrent convolutional neural network that
directly outputs the baseline coordinates. This method allows the direct extraction of baseline coordinates as the output of a neural network without the use of any post-processing steps. We evaluate the model on the cBAD dataset from the ICDAR 2019 competition on baseline detection.

Text Baseline Recognition Using a Recurrent Convolutional Neural Network

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.

Anticipating Activity from Multimodal Signals

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.

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