Italy


In myocardium segmentation of cardiac magnetic resonance images, ambiguities often appear near the boundaries of the target domains due to tissue similarities. To address this issue, we propose a new architecture, called FOANet, which can be decomposed in three main steps: a localization step, a Gaussian-based contrast enhancement step, and a segmentation step. This architecture is supplied with a hybrid loss function that guides the FOANet to study the transformation relationship between the input image and the corresponding label in a three-level hierarchy (pixel-, patch- and map-level), which is helpful to improve segmentation and recovery of the boundaries. We demonstrate the efficiency of our approach on two public datasets in terms of regional and boundary segmentations.

ICPR 2020

FOANet: A Focus of Attention Network with Application to Myocardium Segmentation

structural similarity

fully convolutional network

segmentation

deep learning

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


Gaussian smoothing and its first and second differentials are very important for image processing and computer vision. We already have methods based on the sliding Fourier transform (SFT) in order to calculate Gaussian smoothing efficiently. However, errors in floating point calculation accumulate quickly when we use the single-precision floating-point format. Also, the resultant discontinuities at the edges of SFT intervals generate false extreme points easily. To resolve the above-mentioned problems, we propose the attenuated SFT (ASFT) by introducing a decay factor to the original SFT together with a new criterion for determining coefficients to effectively approximate Gaussian function and its differentials. Extensive experiments demonstrate a decisive superiority of the proposed ASFT over the original SFT.

Stabilized Calculation of Gaussian Smoothing and Its Differentials Using Attenuated Sliding Fourier Transform

In this paper we consider a collection of relative pose problems which arise naturally in applications for visual indoor navigation using unmanned aerial vehicles~(UAVs). We focus on cases where additional information from an onboard IMU is available and thus provides a partial extrinsic calibration through the gravitational vector. 
The solvers are designed for a partially calibrated camera, for a variety of realistic indoor scenarios, which makes it possible to navigate using images of the ground floor. Current state-of-the-art solvers use more general assumptions, such as using arbitrary planar structures; however, these solvers do not yield adequate reconstructions for real scenes, nor do they perform fast enough to be incorporated in real-time systems.

We show that the proposed solvers enjoy better numerical stability, are faster, and require fewer point correspondences, compared to state-of-the-art approaches. These properties are vital components for robust navigation in real-time systems, and we demonstrate on both synthetic and real data that our method outperforms other solvers, and yields superior motion estimation.


Minimal Solvers for Indoor UAV Positioning

we focus on Universal Adversarial Perturbations(UAP)attack on state-of-the-art person re-identification (Re-ID) methods. Existing UAP methods usually compute a perturbation image and add it to the images of interest. Such a simple constant form greatly limits the attack power. To address this problem, we extend the formulation of UAP to a polynomial form and propose the Polynomial Universal Adversarial Perturbation (PUAP). Unlike traditional UAP methods which only rely on the additive perturbation signal, the proposed PUAP consists of both an additive perturbation and a multiplicative modulation factor. The additive perturbation produces the fundamental component of the signal, while the multiplicative factor modulates the perturbation signal in line with the unit impulse pattern of the input image. Moreover, we introduce a Pearson correlation coefficient loss to generate universal perturbations, for disrupting the outputs of person Re-ID models. Extensive experiments on DukeMTMCreID, Market-1501, and MARS show that the proposed method can efficiently improve the attack performance.

Polynomial Universal Adversarial Perturbations for Person Re-Identification

Current research on embodied AI mainly focuses on stand-alone tasks. Instead, we aim at bridging recent findings on embodied exploration and image captioning.
We devise a novel setting involving the exploration of a photorealistic indoor environment while describing the current RGB observation, and we call it Explore and Explain.
We present a novel solution involving curiosity-driven exploration and we apply a captioning algorithm to describe relevant objects along the trajectory of the agent.

Explore and Explain: Self-Supervised Navigation and Recounting

Convolutional Neural Networks (CNNs) have recently been introduced for addressing copy-move forgery detection (CMFD). However, current CMFD CNN-based approaches have insufficient performance commitment regarding the 
localization of the positive class. In this paper, this issue is explored by considering both linear and nonlinear interactions between pixels. A nonlinear Inception module based on second-order Volterra kernels is proposed, in order to ameliorate the results of a state-of-the-art CMFD architecture. The outcome of this work shows that a combination of linear and nonlinear convolution kernels can make the input foreground and background pixels more separable. The proposed approach is evaluated on CASIA and CoMoFoD, two publicly available CMFD datasets, and
results to an improved positive class localization performance. Moreover, the findings of the proposed method imply that the nonlinear Inception module stimulates immense robustness against miscellaneous post processing attacks.

RobusterNet: Improving Copy-Move Forgery Detection with Volterra-based Convolutions

This presentation briefly introduces our motivation and method of utilizing temporal features in dynamic point cloud segmentation. It also presents the experiment results, including comparison to other methods and ablation study on the effect of temporal features.

Human Segmentation with Dynamic LiDAR Data

Visual Question Answering (VQA) is challenging due to the complex cross-modal relations. It has received extensive attention from the research community. From the human perspective, to answer a visual question, one needs to read the question and then refer to the image to generate an answer. This answer will then be checked against the question and image again for the final confirmation. In this paper, we mimic this process and propose a fully attention based VQA architecture. Moreover, an answer-checking module is proposed to perform a unified attention on the jointly answer, question and image representation to update the answer. This mimics the human answer checking process to consider the answer in the context. With answer-checking modules and transferred BERT layers, our model achieves the state-of-the-art accuracy 71.57% using fewer parameters on VQA-v2.0 test-standard split.

Answer-checking in Context: A Multi-modal Fully Attention Network for Visual Question Answering

The ability to handle large scale variations is crucial for many real world visual tasks. A straightforward approach for handling scale in a deep network is to process an image at several scales simultaneously in a set of scale channels. Scale invariance can then, in principle, be achieved by using weight sharing between the scale channels together with max or average pooling over the outputs from the scale channels. The ability of such scale channel networks to generalise to scales not present in the training set over significant scale ranges has, however, not previously been explored. We, therefore, present a theoretical analysis of invariance and covariance properties of scale channel networks and perform an experimental evaluation of the ability of different types of scale channel networks to generalise to previously unseen scales. We identify limitations of previous approaches and propose a new type of foveated scale channel architecture, where the scale channels process increasingly larger parts of the image with decreasing resolution. Our proposed FovMax and FovAvg networks perform almost identically over a scale range of 8, also when training on single scale training data, and do also give improvements in the small sample regime.

Exploring the ability of CNNs to generalise to previously unseen scales over wide scale ranges

Channel attention has shown its great performance recently when it was incorporated into deep convolutional neural networks. However, existing methods usually require extensive computing resources due to their involuted structure, which further increase the computational burden of 3D CNNs. In this paper, a lightweight sparse channel attention (SCA) module implemented by efficient group convolution is proposed, which adopts the idea of sparse channel connection and involves much fewer parameters but brings clear performance gain. Meanwhile, to solve the lack of local channel interaction brought by group convolution, a dominant function called Aggregate-Shuffle-Diverge (ASD) is leveraged to enhance information flow over each group with no additional parameters. We also adjust the existing mainstream 3D CNNs by employing 3D convolution factorization, so as to further reduce the parameters. Our SCA module can be flexibly incorporated into most existing 3D CNNs, all of which can achieve a perfect trade-off between performance and complexity on action recognition task with factorized I3D or 3D ResNext backbone networks. The experimental results also indicate that the resulting network, namely, SCA Net can achieve outstanding performance on UCF-101 and HMDB-51 datasets.

SCA Net: Sparse Channel Attention Module for Action Recognition


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.

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FOANet: A Focus of Attention Network with Application to Myocardium Segmentation

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Stabilized Calculation of Gaussian Smoothing and Its Differentials Using Attenuated Sliding Fourier Transform

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