Italy


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.

ICPR 2020

Channel Planting for Deep Neural Networks Using Knowledge Distillation

neural network architecture search

incremental training

knowledge distillation

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


The fusion of audio and visual modalities is an important stage of audio-visual speech recognition (AVSR), which is generally approached through feature fusion or decision fusion. Feature fusion can exploit the covariations between features from different modalities effectively, whereas decision fusion shows the robustness of capturing an optimal combination of multi- modality. In this work, to take full advantage of the complemen- tarity of the two fusion strategies and address the challenge of inherent ambiguity in noisy environments, we propose a novel hybrid fusion based AVSR method with residual networks and Bidirectional Gated Recurrent Unit (BGRU), which is able to distinguish homophones in both clean and noisy conditions. Specifically, a simple yet effective audio-visual encoder is used to map audio and visual features into a shared latent space to capture more discriminative multi-modal feature and find the internal correlation between spatial-temporal information for different modalities. Furthermore, a decision fusion module is designed to get final predictions in order to robustly utilize the reliability measures of audio-visual information. Finally, we introduce a combined loss, which shows its noise-robustness in learning the joint representation across various modalities. Experimental results on the largest publicly available dataset (LRW) demonstrate the robustness of the proposed method under various noisy conditions.

Robust Audio-Visual Speech Recognition Based on Hybrid Fusion

Adversarial attacks have been a prevalent problem causing misclassification in machine learning models, with stochasticity being a promising direction towards greater robustness. However, stochastic networks frequently underperform compared to deterministic deep networks. In this work, we present a conceptually clear adaptive noise injection mechanism in combination with teacher-initialisation, which adjusts its degree of randomness dynamically through the computation of mini-batch statistics. This mechanism is embedded within a simple framework to obtain stochastic networks from existing deterministic networks. Our experiments show that our method is able to outperform prior baselines under white-box settings, exemplified through CIFAR-10 and CIFAR-100. Following which, we perform in-depth analysis on varying different components of training with our approach on the effects of robustness and accuracy, through the study of the evolution of decision boundary and trend curves of clean accuracy/attack success over differing degrees of stochasticity. We also shed light on the effects of adversarial training on a pre-trained network, through the lens of decision boundaries.

Adaptive Noise Injection for Training Stochastic Student Networks from Deterministic Teachers

In this paper, we present a distinct discriminant
canonical correlation analysis network (DDCCANet) based deep
information quality representation with application to image
classification. Specifically, to explore the sufficient discriminant
information between different data sets, the within-class and
between-class correlation matrices are employed and optimized
jointly. Moreover, different from the existing canonical correlation
analysis network (CCANet) and related algorithms, an
information theoretic descriptor, information quality (IQ), is
adopted to generate the deep-level feature representation for
image classification. Benefiting from the explored discriminant
information and IQ descriptor, it is potential to gain a more
effective deep-level representation from multi-view data sets,
leading to improved performance in classification tasks. To
demonstrate the effectiveness of the proposed DDCCANet, we
conduct experiments on the Olivetti Research Lab (ORL) face
database, ETH80 database and CIFAR10 database. Experimental
results show the superiority of the proposed solution on image
classification.

A Distinct Discriminant Canonical Correlation Analysis Network based Deep Information Quality Representation for Image Classification


The loss function is a key component in deep learning models. A commonly used loss function for classification is the cross-entropy loss, which is a simple yet effective application of information theory for classification problems. Based on this loss, many other loss functions have been proposed, e.g., by adding intra-class and inter-class constraints to enhance the discriminative ability of the learned features. However, these loss functions fail to consider the connections between the feature distribution and the model structure. Aiming at ad- dressing this problem, we propose a channel correlation loss (CC-Loss) that is able to constrain the specific relations be-tween classes and channels as well as maintain the intra-class and the inter-class separability. CC-Loss uses a channel attention module to generate channel attention of features for each sample in the training stage. Next, a Euclidean distance matrix is calculated to make the channel attention vectors associated with the same class become identical and to increase the difference between different classes. Finally, we obtain a feature embedding with good intra-class compactness and inter-class separability. Experimental results show that two different backbone models trained with the proposed CC-Loss outperform the state-of-the-art loss functions on three image classification datasets.

CC-LOSS: Channel Correlation Loss For Image Classification

Cross-domain few-shot classification task (CD-FSC) combines few-shot classification with the requirement to generalize across domains represented by datasets. This setup faces challenges originating from the limited labeled data in each class and, additionally, from the domain shift between training and test sets. 
In this paper, we introduce a novel training approach for existing FSC models. It leverages on the explanation scores, obtained from existing explanation methods when applied to the predictions of FSC models, computed for intermediate feature maps of the models. Firstly, we tailor the layer-wise relevance propagation (LRP) method to explain the predictions of FSC models. Secondly, we develop a model-agnostic explanation-guided training strategy that dynamically finds and emphasizes the features which are important for the predictions. Our contribution does not target a novel explanation method but lies in a novel application of explanations for the training phase. We show that explanation-guided training effectively improves the model generalization. We observe improved accuracy for three different FSC models: RelationNet, cross attention network, and a graph neural network-based formulation, on five few-shot learning datasets: miniImagenet, CUB, Cars, Places, and Plantae. The source code is available at https://github.com/SunJiamei/few-shot-lrp-guided

Explanation-Guided Training for Cross-Domain Few-Shot Classification

Person re-identification aims to identify the same person from different cameras, which needs to integrate whole-body information and capture global correlation. However, convolutional neural network is able to only capture short-distance information because of the size of filters. Self-attention is introduced to capture long-distance correlation, but inner-product similarity calculation in self-attention mingles semantic response and semantic difference together. Semantic difference is more important for person re-identification, because it is robust to illumination without the effect of semantic response. However, we find the scale of norms measuring semantic response is much larger than angle measuring semantic difference by decoupling inner-product similarity into norms and angle. To balance the importance of semantic response and semantic difference in self-attention, we propose the decoupled self-attention module for person re-identification to make the most of self-attention. Extensive experiments show that the decoupled self-attention module obtains significant performance with easier convergence and stronger robustness.

Decoupled Self-attention Module for Person Re-identification


Creating a good looking chart for better visualization is time consuming.
There are plenty of well-designed charts on the Web, which are ideal references for imitation of chart style. However, stored as bitmap images, reference charts have hinder machine interpretation of style settings and thus difficult to be directly applied.
In this paper, we extract visual properties from reference chart images as style templates to restyle charts. We first construct a large-scale dataset of 187,059 chart images from real world data, labeled with predefined visual property values. Then we introduce an end-to-end learning network to extract the properties based on two image-encoding approaches. Furthermore, in order to capture spatial relationships of chart objects, which are crucial in solving the task, Experimental results show that our model significantly outperforms baseline models. By adding positional features, our model achieves better performance. Finally, we present the application for chart restyling based on our model.

Visual Style Extraction from Chart Images for Chart Restyling

From a spherical image, a gaze point, instead of
gaze vectors, can be estimated directly because a remote spherical
camera can observe a user’s face and a gaze target simultane-
ously. This paper investigates the problem of estimating a gaze
point in a spherical image from facial landmarks. In contrast with
the existing methods which usually assume gaze points move on
a narrow plane, the proposed method can cope with the situation
where gaze points vary in depth for a relatively wide field of view. (a) As shown in the results of comparative experiments, we find the
orthogonal coordinates of facial landmarks on a unit sphere is
a reasonable representation in comparison with spherical polar
coordinates; the cues of head pose is helpful to improve the
accuracy of gaze points. Consequently, the proposed method
achieves a performance on the accuracy of gaze points estimation
which is comparable to the state of the art methods.

Estimating Gaze Points from Facial Landmarks by a Remote Spherical Camera

A new base-derivative framework is proposed, where base refers to the original visible and infrared modalities, and derivative refers to the two auxiliary modalities that are derived from base. 
In the proposed framework, the double-modality cross-modal learning problem is reformulated as a four-modality one. After that, the images of all the base and derivative modalities are fed into the feature learning network. With the doubled input images, the learned person features become more discriminative.
Furthermore, the proposed framework is optimized by the enhanced intra- and cross-modality constraints with the assistance of two derivative modalities.

A Base-Derivative Framework for Cross-Modality RGB-Infrared Person Re-Identification

Deep neural network based object detection has become the cornerstone of many real-world applications. Along with this success comes concerns about its vulnerability to malicious attacks. To gain more insight into this issue, we propose a contextual camouflage attack (CCA for short) algorithm to influence the performance of object detectors. In this paper, we use an evolutionary search strategy and adversarial machine learning in interactions with a photo-realistic simulated environment to find camouflage patterns that are effective over a huge variety of object locations, camera poses, and lighting conditions. The proposed camouflages are validated effective to most of the state-of-the-art object detectors.

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Channel Planting for Deep Neural Networks Using Knowledge Distillation

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