Italy

Scene text detection (STD) aims to locate text in
images and plays an important role in many computer vision
tasks including automatic driving and text recognition systems.
Recently, deep neural networks (DNNs) have been widely and
successfully used in scene text detection, leading to plenty
of DNN-based STD methods including regression-based and
segmentation-based STD methods. However, recent studies have
also shown that DNN is vulnerable to adversarial attacks, which
can significantly degrade the performance of DNN models. In
this paper, we investigate the robustness of DNN-based STD
methods against adversarial attacks. To this end, we propose
a generic and efficient attack method to generate adversarial
examples, which are produced by adding small but imperceptible
adversarial perturbation to the input images. Experiments on
attacking four various models and a real-world STD engine of
Google optical character recognition (OCR) show that the stateof-the-art DNN-based STD methods including regression-based
and segmentation-based methods are vulnerable to adversarial
attacks.

ICPR 2020

Transferable Adversarial Attacks for Deep SceneText Detection

deep scene text detection

deep learning

adversarial attacks

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

Dense pixel matching is required for many computer vision algorithms such as disparity, optical flow or scene flow estimation. Feature Pyramid Networks (FPN) have proven to be a suitable feature extractor for CNN-based dense matching tasks. FPN generates well localized and semantically strong features at multiple scales. However, the generic FPN is not utilizing its full potential, due to its reasonable but limited localization accuracy. Thus, we present ResFPN - a multi-resolution feature pyramid network with multiple residual skip connections, where at any scale, we leverage the information from higher resolution maps for stronger and better localized features. In our ablation study, we demonstrate the effectiveness of our novel architecture with clearly higher accuracy than FPN. In addition, we verify the superior accuracy of ResFPN in many different pixel matching applications on established datasets like KITTI, Sintel, and FlyingThings3D.

ResFPN: Residual Skip Connections in Multi-Resolution Feature Pyramid Networks for Accurate Dense Pixel Matching

Deep convolutional neural networks (CNNs) have recently achieved great success for single image super-resolution (SISR) task due to their powerful feature representation capabilities. The most recent deep learning based SISR methods focus on designing deeper / wider models to learn the non-linear mapping between low-resolution (LR) inputs and high-resolution (HR) outputs. These existing SR methods do not take into account the image observation (physical) model and thus require a large number of network's trainable parameters with a great volume of training data. To address these issues, we propose a deep Iterative Super-Resolution Residual Convolutional Network (ISRResCNet) that exploits the powerful image regularization and large-scale optimization techniques by training the deep network in an iterative manner with a residual learning approach. Extensive experimental results on various super-resolution benchmarks demonstrate that our method with a few trainable parameters improves the results for different scaling factors in comparison with the state-of-art methods.

Deep Iterative Residual Convolutional Network for Single Image Super-Resolution

In the last few years, several techniques for facial
manipulation in videos have been successfully developed and
made available to the masses (i.e., FaceSwap, deepfake, etc.).
These methods enable anyone to easily edit faces in video
sequences with incredibly realistic results and a very little effort.
Despite the usefulness of these tools in many fields, if used
maliciously, they can have a significantly bad impact on society
(e.g., fake news spreading, cyber bullying through fake revenge
porn). The ability of objectively detecting whether a face has
been manipulated in a video sequence is then a task of utmost
importance.
In this paper, we tackle the problem of face manipulation detection in video sequences targeting modern facial manipulation
techniques. In particular, we study the ensembling of different
trained Convolutional Neural Network (CNN) models. In the
proposed solution, different models are obtained starting from a
base network (i.e., EfficientNetB4) making use of two different
concepts: (i) attention layers; (ii) siamese training. We show that
combining these networks leads to promising face manipulation
detection results on two publicly available datasets with more
than 119000 videos.

Video Face Manipulation Detection Through Ensemble of CNNs

With the advent of data living on vertices of graphs, there is much interest in processing the so-called graph signals for partitioning tasks. As active contours have had much impact in the image processing community, their formulation on graphs is of importance to the field of graph signal processing. This paper proposes an adaptation on graphs of a model that combines the Geodesic Active Contour and the Active Contour Without Edges models. In addition, specific terms depending on graphs are introduced in the formulation. This adaptation is solved using a level set formulation with a gradient descent that can be expressed as a morphological front evolution process. Experimental results on different kinds of graphs signals show the benefit of the approach.

Graph signal active contours

Studying the spatial organization of objects in images is fundamental to increase both the understanding of a sensed scene and the explainability of the perceived similarity between images. This leads to the fundamental problem of handling spatial relations: given two objects depicted in an image, or two parts in an object, how to extract and describe efficiently their spatial configuration? Dedicated descriptors already exist for this task, like the efficient force histogram. In this article, we introduce the Force Banner, which extends it to two dimensions by using a panel of forces (attraction and repulsion), so as to benefit from more expressiveness and to model rich spatial information. This descriptor can be used as an intermediate representation of the image dedicated to the spatial configuration, and feed a classical 2D Convolutional Neural Network (CNN) to benefit from their powerful performances. As an illustration of this, we used it to solve a classification problem aiming to discriminate simple spatial relations, but with variable configuration complexities. Experimental results obtained on datasets of images with various shapes highlight the interest of this approach, in particular for complex spatial configurations.

Force Banner for the recognition of spatial relations

Recent co-part segmentation methods mostly operate in a supervised learning setting, which requires a large amount of annotated data for training. To overcome this limitation, we propose a self-supervised deep learning method for co-part segmentation. Differently from previous works, our approach develops the idea that motion information inferred from videos can be leveraged to discover meaningful object parts. To this end, our method relies on pairs of frames sampled from the same video. The network learns to predict part segments together with a representation of the motion between two frames, which permits reconstruction of the target image. Through extensive experimental evaluation on publicly available video sequences we demonstrate that our approach can produce improved segmentation maps with respect to previous self-supervised co-part segmentation approaches.

Motion supervised co-part segmentation

Image denoising is an essential part of many image processing and computer vision tasks due to inevitable noise corruption during image acquisition. Traditionally, many researchers have investigated image priors for the denoising, within the Bayesian perspective based on image properties and statistics. Recently, deep convolutional neural networks (CNNs) have shown great success in image denoising by incorporating large-scale synthetic datasets. However, they both have pros and cons. While the deep CNNs are powerful for removing the noise with known statistics, they tend to lack flexibility and practicality for the blind and real-world noise. Moreover, they cannot easily employ explicit priors. On the other hand, traditional non-learning methods can involve explicit image priors, but they require considerable computation time and cannot exploit large-scale external datasets. In this paper, we present a CNN-based method that leverages the advantages of both methods based on the Bayesian perspective. Concretely, we divide the blind image denoising problem into sub-problems and conquer each inference problem separately. As the CNN is a powerful tool for inference, our method is rooted in CNNs and propose a novel design of network for efficient inference. With our proposed method, we can successfully remove blind and real-world noise, with a moderate number of parameters of universal CNN.

Deep Universal Blind Image Denoising

Deep learning has been widely used in natural image processing, but its application for medical data still needs further investigation. So we focus on nuclear detection and segmentation of pathological images and build an end-to-end network. In order to meet the needs of both nuclear detection and segmentation, our contribution consists of two parts. We will show the design details and effects in this presentation.

Introduction of A Triplet-path Dilated Network for Detection and Segmentation of General Pathological Images

We propose a novel multi-task framework, which can provide lung nodule segmentation mask, malignancy prediction, and medical features for interpretable diagnosis at the same time. Our framework mainly contains two sub-network: (1) Multi-Channel Segmentation Sub-network (MSN) for lung nodule segmentation, and (2) Joint Classification Sub-network (JCN) for interpretable lung nodule diagnosis. In the proposed framework, we use U-Net down-sampling processes for extracting low-level deep learning features, which are shared by two sub-networks. The JCN forces the down-sampling processes to learn better low-level deep features, which lead to a better construct of segmentation masks. We evaluate the proposed framework on the public dataset LIDC-IDRI. Our framework achieves a promising Dice score of 86.43% in segmentation, 87.07% in malignancy level prediction, and convincing results in interpretable medical feature predictions.

End-to-End Multi-Task Learning for Lung Nodule Segmentation and Diagnosis

We propose a GAN-based image compression method working at extremely low bitrates below 0.1bpp. Most existing learned image compression methods suffer from blur at extremely low bitrates. Although GAN can help to reconstruct sharp images, there are two drawbacks. First, GAN makes training unstable. Second, the reconstructions often contain unpleasing noise or artifacts. To address both of the drawbacks, our method adopts two-stage training and network interpolation. The two-stage training is effective to stabilize the training. Moreover, the network interpolation utilizes the models in both stages and reduces undesirable noise and artifacts, while maintaining important edges. Hence, we can control the trade-off between perceptual quality and fidelity without re-training models. The experimental results show that our model can reconstruct high-quality images. Furthermore, our user study confirms that our reconstructions are preferable to the state-of-the-art GAN-based image compression model.

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