Italy

Deep learning technology has enabled successful modeling of complex facial features when high quality images are available. Nonetheless, accurate modeling and recognition of human faces in real world scenarios &#39;on the wild&#39; or under adverse conditions remains an open problem. When unconstrained faces are mapped into deep features, variations such as illumination, pose, occlusion, etc., can create inconsistencies in the resultant feature space. Hence, deriving conclusions based on direct associations could lead to degraded performance. This rises the requirement for a basic feature space analysis prior to face recognition. This paper devises a joint clustering and classification scheme which learns deep face associations in an easy-to-hard way. Our method is based on hierarchical clustering where the early iterations tend to preserve high reliability. The rationale of our method is that a reliable clustering result can provide insights on the distribution of the feature space, that can guide the classification that follows. Experimental evaluations on three tasks, face verification, face identification and rank-order search, demonstrates better or competitive performance compared to the state-of-the-art, on all three experiments.

ICPR 2020

ClusterFace: Joint Clustering and Classification for Set-Based Face Recognition

face recognition

clustering

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. 


Deep learning technology has enabled successful modeling of complex facial features when high quality images are available. Nonetheless, accurate modeling and recognition of human faces in real world scenarios 'on the wild' or under adverse conditions remains an open problem. When unconstrained faces are mapped into deep features, variations such as illumination, pose, occlusion, etc., can create inconsistencies in the resultant feature space. Hence, deriving conclusions based on direct associations could lead to degraded performance. This rises the requirement for a basic feature space analysis prior to face recognition. This paper devises a joint clustering and classification scheme which learns deep face associations in an easy-to-hard way. Our method is based on hierarchical clustering where the early iterations tend to preserve high reliability. The rationale of our method is that a reliable clustering result can provide insights on the distribution of the feature space, that can guide the classification that follows. Experimental evaluations on three tasks, face verification, face identification and rank-order search, demonstrates better or competitive performance compared to the state-of-the-art, on all three experiments.

poster

Autonomous driving of a personal mobility vehicle such as a wheelchair in a walking space is crucial as a means of transportation for the elderly and the physically handicapped. To realize this, accurate pedestrian detection is indispensable. As existing 3D object detection methods are trained with a roadway dataset, they are widely used for object detection in roadways. These methods have two major drawbacks as regards the detection of objects in walking spaces. The first is that they largely depend on the different LIDAR models. To eliminate this issue, we propose a 3D object detection method, CosPointPillars, that does not take the reflection intensities of the LIDAR point cloud, which causes a sensor model dependency, as input. The second drawback is that networks trained with a roadway dataset cannot sufficiently detect pedestrians (who are major traffic participants in walking spaces) located within a short distance; this is because the roadway dataset hardly includes nearby pedestrians. To solve this issue, we generated a new walking space dataset called SimDataset, which includes nearby pedestrians as a training dataset in the simulations. An experiment on a real walking space showed that SimDataset is suitable for use in pedestrian detection.

Sensor-independent Pedestrian Detection for Personal Mobility Vehicles in Walking Space Using Dataset Generated by Simulation

Unsupervised domain adaptation has achieved significant results by leveraging knowledge from a source domain to learn a related but unlabeled target domain. Previous methods are insufficient to model domain discrepancy and class discrepancy, which may lead to misalignment and poor adaptation performance. To address this problem, in this paper, we propose a unified framework, called distance-aware domain adaptation, which is fully aware of both cross-domain distance and class-discriminative distance. In addition, second-order statistics distance and manifold alignment are also exploited to extract more information from data. In this manner, the generalization error of the target domain in classification problems can be reduced substantially. To validate the proposed method, we conducted experiments on five public datasets and an ablation study. The results demonstrate the good performance of our proposed method.

A Unified Framework for Distance-Aware Domain Adaptation


In this paper, a new recursive structure based
on the convolution model of discrete cosine transform (DCT)
for designing of a finite impulse response (FIR) digital filter
is proposed. In our derivation, we start with the convolution
model of DCT-II to use its Z-transform for the proposed filter
structure perspective. Moreover, using the same algorithm, a
filter base implementation of the inverse DCT (IDCT) for image
reconstruction is developed. The computational time experiments
of the proposed DCT/IDCT filter(s) demonstrate that the proposed
filters achieve faster elapsed CPU time compared to the
others. The image filtering and reconstruction performance of
the proposed approach on ultrasound images are presented to
validate the theoretical framework.

DCT/IDCT Filter Design for Ultrasound Image Filtering

We study the problem of unsupervised domain adaptation that aims at obtaining a prediction model for the target domain using labeled data from the source domain and unlabeled data from the target domain. There exists an array of recent research based on the idea of extracting features that are not only invariant for both domains but also provide high discriminative power for the target domain. In this paper, we propose an idea of improving the discriminativeness: Adding an extra artificial class and training the model on the given data together with the GAN-generated samples of the new class. The trained model based on the new class samples is capable of extracting the features that are more discriminative by repositioning data of current classes in the target domain and therefore increasing the distances among the target clusters in the feature space. Our idea is highly generic so that it is compatible with many existing methods such as DANN, VADA, and DIRT-T. We conduct various experiments for the standard data commonly used for the evaluation of unsupervised domain adaptations and demonstrate that our algorithm achieves the SOTA performance for many scenarios.

Enlarging Discriminative Power by Adding an Extra Class in Unsupervised Domain Adaptation

Estimated Time of Arrival (ETA) is one of the most fundamental problems in Intelligent Transportation System. It is considered as predicting the travel time from the origin to the destination along a given path. The route consists of a sequence of links. One of the key techniques is to use embedding vectors to represent links. However, the embedding suffers from the data sparsity problem that many links are traversed by too few floating cars even in large ride-hailing platforms. To address the problem, we propose the Road Network Metric Learning framework for ETA (RNML-ETA). We transfer the knowledge of hot links to the cold links by metric learning. In this presentation, we will introduce the key points of RNML-ETA which is the first deep learning method that effectively addresses the road network data sparsity problem. On the large scale real-world datasets, our method outperforms the state-of-the-art model and the promotion concentrates on the cold links with few data.

Road Network Metric Learning for Estimated Time of Arrival


Recent works have made great progress in semantic segmentation by exploiting contextual information in a local or global manner with dilated convolutions, pyramid pooling or self-attention mechanism. However, few works have focused on harvesting boundary information to improve the segmentation performance. In order to enhance the feature similarity within the object and keep discrimination from other objects, we propose a boundary-aware graph convolution (BGC) module to propagate features within the object. The graph reasoning is performed among pixels of the same object apart from the boundary pixels. Based on the proposed BGC module, we further introduce the Boundary-aware Graph Convolution Network(BGCNet), which consists of two main components including a basic segmentation network and the BGC module, forming a coarse-to-fine paradigm. Specifically, the BGC module takes the coarse segmentation feature map as node features and boundary prediction to guide graph construction. After graph convolution, the reasoned feature and the input feature are fused together to get the refined feature, producing the refined segmentation result. We conduct extensive experiments on three popular semantic segmentation benchmarks including Cityscapes, PASCAL VOC 2012 and COCO Stuff, and achieve state-of-the-art performance on all three benchmarks.


Boundary-aware Graph Convolution for Semantic Segmentation


Zero-shot hashing aims at learning hashing model from seen classes and the obtained model is capable of generalizing to unseen classes for image retrieval. Inspired by zero-shot learning, existing zero-shot hashing methods usually transfer the supervised knowledge from seen to unseen classes, by embedding the hamming space to a shared semantic space. However, this makes instances difficult to distinguish due to limited hashing bit numbers, especially for semantically similar unseen classes. We propose a novel inductive zero-shot hashing framework, i.e., VSB2-Net, where both semantic space and visual feature space are embedded to the same hamming space instead. The reconstructive semantic relationships are established in the hamming space, preserving local similarity relationships and explicitly enlarging the discrepancy between semantic hamming vectors. A two-task architecture, comprising of classification module and visual feature reconstruction module, is employed to enhance the generalization and transfer abilities. Extensive evaluation results on several benchmark datasets demonstrate the superiority of our proposed method compared to several state-of-the-art baselines.

VSB2-Net: Visual-Semantic Bi-Branch Network for Zero-Shot Hashing

Facial Action Units (AUs) are of great significance in communication. Automatic AU detection can improve the understanding of psychological condition and emotional status. Recently, a number of deep learning methods have been proposed to take charge with problems in automatic AU detection.
Several challenges, like unbalanced labels and ignorance of local information, remain to be addressed. In this paper, we propose a fast and light neural network called MRP-Net, which is an end-to-end trainable method for facial AU detection to solve these problems. First, we design a Multiple Region Perception (MRP) module aimed at capturing different locations and sizes of features in the deeper level of the network without facial landmark points. Then, in order to balance the positive and negative samples in the large dataset, a batch balanced method adjusting the weight of every sample in one batch in our loss function is suggested. Experimental results on two popular AU datasets, BP4D and DISFA prove that MRP-Net outperforms state-of-the-art methods. Compared with the best method, not only does MRP-Net have an average F1 score improvement of
2.95% on BP4D and 5.43% on DISFA, and it also decreases the number of network parameters by 54.62% and the number of network FLOPs by 19.6%.


MRP-Net: A Light Multiple Region Perception Neural Network for Multi-label AU Detection


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

Generalized zero-shot learning (GZSL) tackles the problem of learning to classify instances involving both seen classes and unseen ones. The key issue is how to effectively transfer the model learned from seen classes to unseen classes. Existing works in GZSL usually assume that some prior information about unseen classes are available. However, such an assumption is unrealistic when new unseen classes appear dynamically. To this end, we propose a novel heterogeneous graph-based knowledge transfer method (HGKT) for GZSL, agnostic to unseen classes and instances, by leveraging graph neural network. Specifically, a structured heterogeneous graph is constructed with high-level representative nodes for seen classes, which are chosen through Wasserstein barycenter in order to simultaneously capture inter-class and intra-class relationship. The aggregation and embedding functions can be learned through graph neural network, which can be used to compute the embeddings of unseen classes by transferring the knowledge from their neighbors. Extensive experiments on public benchmark datasets show that our method achieves state-of-the-art results.

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ClusterFace: Joint Clustering and Classification for Set-Based Face Recognition

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Sensor-independent Pedestrian Detection for Personal Mobility Vehicles in Walking Space Using Dataset Generated by Simulation

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