Italy

Recent research has revealed that deep neural networks are highly vulnerable to adversarial examples. In this paper, different from most adversarial attacks which directly modify pixels in spatial domain, we propose a novel black-box attack in frequency domain, named as \textit{f-mixup}, based on the property of natural images and perception disparity between human-visual system (HVS) and convolutional neural networks (CNNs): First, natural images tend to have the bulk of their Fourier spectrums concentrated on the low frequency domain; Second, HVS is much less sensitive to high frequencies while CNNs can utilize both low and high frequency information to make predictions. Extensive experiments are conducted and show that deeper CNNs tend to concentrate more on the higher frequency domain, which may explain the contradiction between robustness and accuracy. In addition, we compared \textit{f-mixup} with existing attack methods and observed that our approach possesses great advantages. Finally, we show that \textit{f-mixup} can be also incorporated in training to make deep CNNs defensible against a kind of perturbations effectively.

ICPR 2020

F-mixup: Attack CNNs From Fourier Perspective

frequency domain

black-box attack

deep learning

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The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

poster

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. 


Visual tracking serves as a significant module in many applications. However, the heavy computation and low speed of many recent trackers restrict their applications in some computing power restricted scenarios. On the other hand, the simple update scheme of most correlation filter based trackers limits their robustness during target deformation and occlusion. In this paper, we explore the update scheme of correlation filter based trackers and propose an efficient and adaptive training sample update scheme. Training sample extracted in each frame is updated to the training set according to its distance between existing samples measured with difference hashing algorithm(DHA) or discarded according to tracking result reliability. Experiments on OTB-2015, Temple Color 128 and UAV123 demonstrate our tracker performs favourably against state-of-the-art trackers with light computation and runs over 100 fps on desktop computer with Intel i7-8700 CPU(3.2GHz).

Efficient correlation filter tracking with adaptive training sample update scheme


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.

Enhanced User Interest and Expertise Modeling for Expert Recommendation

Predicting which part of a scene elderly people would pay attention to could be useful in assisting their daily activities, such as driving, walking, and searching. Many computational models for predicting focus of attention (FoA) have
been developed. However, most of them focus on mimicking adult FoA and do not work well for predicting elderly’s, due to age-related changes in human vision. Is it possible to leverage the prediction results made by an FoA model of general adults to accurately predict elderly’s FoA, rather than training a new network from scratch? In this paper, we consider a novel problem of translating adult’s FoA to elderly’s and propose an approach based on deep image-to-image translation. Our model is trained by minimizing both Kullback-Leibler divergence and adversarial loss to approximate the joint probability distribution of adult and elderly FoA. Experiments on two datasets demonstrate that our model gives remarkable prediction accuracy.

Translating Adult’s Focus of Attention to Elderly’s

In supervised classiﬁcation, the learning process typically trains a classiﬁer to optimize the accuracy of classifying data into the classes that appear in the learning set, and only them. While this framework ﬁts many use cases, there are situations where the learning process is knowingly performed using a learning set that only represents the data that have been observed so far among a virtually unconstrained variety of possible samples. It is then crucial to deﬁne a classiﬁer which has the ability to reject a sample, i.e., to classify it into a rejection class that has not been yet deﬁned. Although obvious solutions can add this ability a posteriori to a classiﬁer that has been learned classically, a better approach seems to directly account for this requirement in the classiﬁer design. In this presentation, we propose an innovative learning strategy for supervised classiﬁcation that is able, by design, to reject a sample as not belonging to any of the known classes. For that, we rely on modeling each class as the combination of a probability density function (PDF) and a threshold that is computed with respect to the other classes. Several alternatives are compared and the best result is presented in this framework.

How to deﬁne a rejection class based on model learning?

Existing deep siamese trackers are typically built on off-the-shelf CNN models for feature learning, with the demand for huge power consumption and memory storage. This limits current deep siamese trackers to be carried on resource-constrained devices like mobile phones. In this work, we address this issue by presenting a novel Distilled Learning Framework(DLF) for siamese tracking, which aims at learning tracking models with efficiency and high accuracy. Specifically, we propose two simple yet effective knowledge distillation strategies, denote as point-wise distillation and pairwise distillation, which are designed for transferring knowledge from a more discriminative teacher tracker into a compact student tracker. In this way, cost-effective and high performance tracking could be achieved. Extensive experiments on several tracking benchmarks demonstrate the effectiveness of our proposed method.

Exploiting Distilled Learning for Deep Siamese Tracking


Group retrieval has attracted plenty of attention in artificial intelligence, traditional group retrieval researches assume that members in a group are unique and do
not change under different cameras. However, the assumption may not be met for practical situations such as open-world and group-ambiguity scenarios. This paper tackles an important yet non-studied problem: re-identifying changing groups of people under the open-world and group-ambiguity scenarios in different camera fields. The open-world scenario considers that there are probably non-target
people for the probe set appear in the searching gallery, while the group-ambiguity scenario means the group members may change. The open-world and group-ambiguity issue is very challenging for the existing methods because the changing of group members results in dramatic visual variations. Nevertheless, as far as we know, the existing literature lacks benchmarks which target on coping with this issue. In this paper, we propose a new group retrieval dataset named OWGA-Campus to consider these challenges. Moreover, we propose a person-to-group similarity matching based ambiguity removal (P2GSM-AR) method to solve these
problems and realize the intention of group retrieval. Experimental results on OWGA-Campus dataset demonstrate the effectiveness and robustness of the proposed P2GSM-AR approach in improving the performance of the state-of-the-art feature extraction methods of person re-id towards the open-world and ambiguous group retrieval task.

Open-World Group Retrieval with Ambiguity Removal: A Benchmark

As a cross modality retrieval task, RGB-infrared person re-identification(Re-ID) is an important and challenging tasking, because of its important role in video surveillance applications and large cross-modality variations between visible and infrared images. Most previous works addressed the problem of cross-modality gap with feature alignment by original feature representation learning straightly. In this paper, different from existing works, we propose a novel network(CE2L) to tackle the
cross-modality gap with feature alignment. CE2L mainly focuses on adding discriminative information and learning robust features by converting modality between visible and infrared images. Its merits are highlighted in two aspects: 1)Using CycleGAN to convert infrared images into color images can not only increase the recognition characteristics of images, but also allow the our network
to better learn the two modal image features; 2)Our novel method can serve as data augmentation. Specifically, it can increase data diversity and total data against over-fitting by converting labeled training images to another modal images. Extensive experimental results on two datasets demonstrate superior performance compared to the baseline and the state-of-the-art methods.

RGB-Infrared Person Re-identification via Image Modality Conversion

Facial expression recognition (FER), aiming to classify the expression present in the facial image or video, has attracted a lot of research interests in the field of artificial
intelligence and multimedia. In terms of video based FER task, it is sensible to capture the dynamic expression variation among the frames to recognize facial expression. However, existing methods directly utilize CNN-RNN or 3D CNN to extract the spatial-temporal features from different facial units, instead of concentrating on a certain region during expression variation capturing, which leads to limited performance in FER. In our paper, we introduce a Graph Convolutional Network (GCN) layer into a common CNN-RNN based model for video-based FER. First, the GCN layer is utilized to learn more significant facial expression features which concentrate on certain regions after sharing information between extracted CNN features of nodes. Then, a LSTM layer is applied to learn long-term dependencies among the GCN learned features to model the variation. In addition, a weight assignment mechanism is also designed to weight the output of different nodes for final classification by characterizing the expression intensities in each frame. To the best of our knowledge, it is the first time to use GCN in FER task. We evaluate our method on three widely-used datasets, CK+, Oulu-CASIA and MMI, and also one challenging wild dataset AFEW8.0, and the experimental results demonstrate that our method has superior performance to existing methods.

Video-based Facial Expression Recognition using Graph Convolutional Networks

This paper proposed a subspace projection hashing (SPH) to enable the cross-spectrum face recognition task. The intrinsic idea behind SPH is to project the features from different domains onto a \emph{common subspace}, where matching the faces from different domains can be accomplished. Notably, we proposed a new loss function that can (i) preserve both inter-domain and intra-domain similarity; (ii) regularize a scaled-up pairwise distance between hashed codes, to optimize projection matrix. Three datasets, Wiki, EURECOM VIS-TH paired face and TDFace are adopted to evaluate the proposed SPH. The experimental results indicate that the proposed SPH outperforms the original linear subspace ranking hashing (LSRH) in the benchmark dataset (Wiki) and demonstrates a reasonably good performance for visible-thermal, visible-near-infrared face recognition, therefore suggests the feasibility and effectiveness of the proposed SPH.

Cross-spectrum Face Recognition Using Subspace Projection Hashing


We propose a light-weight variational framework for online tracking of object segmentations in videos based on optical flow and image boundaries.
While high-end computer vision methods on this task rely on sequence specific training of dedicated CNN architectures, we show the potential of a variational model, based on generic video information from motion and color. Such cues are usually required for tasks such as robot navigation or grasp estimation. We leverage them directly for video object segmentation and thus provide accurate segmentations at potentially very low extra cost. 
Our simple method can provide competitive results compared to the costly CNN-based methods with parameter tuning.
Furthermore, we show that our approach can be combined with state-of-the-art CNN-based segmentations in order to improve over their respective results. We evaluate our method on the datasets DAVIS 16,17 and SegTrack v2.

F-mixup: Attack CNNs From Fourier Perspective

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Efficient correlation filter tracking with adaptive training sample update scheme

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