Italy

The notion of anchor plays a major role in modern detection algorithms such as the Faster-RCNN or the SSD detector. Anchors relate the features of the last layers of the detector with bounding boxes containing objects in images. Despite their importance, the literature on object detection has not paid real attention to them. The motivation of this paper comes from the observations that (i) each anchor learns to classify and regress candidate objects independently (ii) insufficient examples are available for each anchor in case of small-scale datasets. This paper addresses these questions by proposing a novel hierarchical head for the SSD detector. The new design has the added advantage of no extra weights, as compared to the original design at inference time, while improving detectors performance for small size training sets. Improved performance on PASCAL-VOC and state-of-the-art performance on FlickrLogos-47 validate the method. We also show when the proposed design does not give additional performance gain over the original design.

ICPR 2020

Hierarchical Head Design for Object Detectors

anchors

2d object detection

computer vision

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

Objects from correlated classes usually share highly similar appearance while objects from uncorrelated classes are very different. Most of current image recognition works treat each class independently, which ignores these class correlations and inevitably leads to sub-optimal performance in many cases. Fortunately, object classes inherently form a hierarchy with different levels of abstraction and this hierarchy encodes rich correlations among different classes. In this work, we utilize a soft label vector that encodes the prior knowledge of class correlations as extra regularization to train the image classifiers. Specifically, for each class, instead of simply using a one-hot vector, we assign a high value to its correlated classes and assign small values to those uncorrelated ones, thus generating knowledge embedded soft labels. We conduct experiments on both general and fine-grained image recognition benchmarks and demonstrate its superiority compared with existing methods.

Exploiting Knowledge Embedded Soft Labels for Image Recognition

The detection of logos in images, for instance, logos of airlines on airplane tails, is a difficult task in real-world weather conditions. Most systems used for logo detection are very good at detecting logos in clean images. However, they exhibit problems when images are degraded by effects of adverse weather conditions as they frequently occur in real-world scenarios. For investigating this problem on airline logo detection as a sub-problem of logo detection, we first present a new dataset for
airline logo detection on airplane tails containing a test split with images degraded by adverse weather effects. Second, to handle the detection of airline logos effectively, a new two-stage airline logo detection system based on a state-of-the-art object proposal generation system and a specifically tailored classifier is proposed. Finally, improving the results on images degraded by adverse weather effects, we introduce a learning-free application-agnostic data augmentation strategy simulating effects like rain and fog. The results show the superior performance of our airline logo detection system compared to state-of-the-art. Furthermore, applying our data augmentation approach to a variety of systems, reduces the significant drop in performance on degraded images.

Which Airline is This? Airline Logo Detection in Real-World Weather Conditions

Camera pose estimation in large-scale environments is still an open question and, despite recent promising results, it may still fail in some situations. The research so far has focused on improving subcomponents of estimation pipelines, to achieve more accurate poses. However, there is no guarantee for the result to be correct, even though the correctness of pose estimation is critically important in several visual localization applications, such as in autonomous navigation.
In this paper we bring to attention a novel research question, pose confidence estimation, where we aim at quantifying how reliable the visually estimated pose is.
We develop a novel confidence measure to fulfill this task and show that it can be flexibly applied to different datasets, indoor or outdoor, and for various visual localization pipelines.
We also show that the proposed techniques can be used to accomplish a secondary goal: improving the accuracy of existing pose estimation pipelines.
Finally, the proposed approach is computationally light-weight and adds only a negligible increase to the computational effort of pose estimation.

Can You Trust Your Pose? Confidence Estimation in Visual Localization


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

Many image and vision applications require a large amount of data for model training. Collecting all such data at a central location can be challenging due to data privacy and communication bandwidth restrictions. Federated learning is an effective way of training a machine learning model in a distributed manner from local data collected by client devices, which does not require exchanging the raw data among clients. A challenge is that among the large variety of data collected at each client, it is likely that only a subset is relevant for a learning task while the rest of data has a negative impact on model training. Therefore, before starting the learning process, it is important to select the subset of data that is relevant to the given federated learning task. In this paper, we propose a method for distributedly selecting relevant data, where we use a benchmark model trained on a small benchmark dataset that is task-specific, to evaluate the relevance of individual data samples at each client and select the data with sufficiently high relevance. Then, each client only uses the selected subset of its data in the federated learning process. The effectiveness of our proposed approach is evaluated on multiple real-world image datasets in a simulated system with a large number of clients, showing up to 25% improvement in model accuracy compared to training with all data.

Overcoming Noisy and Irrelevant Data in Federated Learning

Novel view synthesis is a challenging problem in computer vision and robotics. Different from the existing works, which need the reference images or 3D models of the scene to generate images under novel views, we propose a novel paradigm to this problem. That is, we synthesize the novel view from only a 6-DoF camera pose directly. Although this setting is the most straightforward way, there are few works addressing it. While, our experiments demonstrate that, with a concise CNN, we could get a meaningful parametric model that could reconstruct the correct scenery images only from the 6-DoF pose. To this end, we propose a two-stage learning strategy, which consists of two consecutive CNNs: GenNet and RefineNet. GenNet generates a coarse image from a camera pose. RefineNet is a generative adversarial network that refines the coarse image. In this way, we decouple the geometric relationship between mapping and texture detail rendering. Extensive experiments conducted on the public datasets prove the effectiveness of our method. We believe this paradigm is of high research and application value and could be an important direction in novel view synthesis.

Novel View Synthesis from only a 6-DoF Camera Pose by Two-stage Networks

Research on facial action unit detection has shown remarkable performances by using deep spatial learning models in recent years, however, it is far from reaching its full capacity in learning due to the lack of use of temporal information of AUs across time. Since the AU occurrence in one frame is highly likely related to previous frames in a temporal sequence, exploring temporal correlation of AUs across frames becomes a key motivation of this work. In this paper, we propose a novel temporal fusion and AU-supervised self-attention network (a so- called SAT-Net) to address the AU detection problem. First of all, we input the deep features of a sequence into a convolutional LSTM network and fuse the previous temporal information into the feature map of the last frame, and continue to learn the AU occurrence. Second, considering the AU detection problem is a multi-label classification problem that individual label depends only on certain facial areas, we propose a new self-learned attention mask by focusing the detection of each AU on parts of facial areas through the learning of individual attention mask for each AU, thus increasing the AU independence without the loss of any spatial relations. Our extensive experiments show that the proposed framework achieves better results of AU detection over the state-of-the-arts on two benchmark databases (BP4D and DISFA).

SAT-Net: Self-Attention and Temporal Fusion for Facial Action Unit Detection

Deep learning classifiers are now known to have flaws in the representations of their class. Adversarial attacks can find a human-imperceptible perturbation for a given image that will mislead a trained model. The most effective methods to defend against such attacks trains on generated adversarial examples to learn their distribution. 
Previous work aimed to align original and adversarial image representations in the same way as domain adaptation to improve robustness. Yet, they partially align the representations using approaches that do not reflect the geometry of space and distribution.
In addition, it is difficult to accurately compare robustness between defended models. Until now, they have been evaluated using a fixed perturbation size. However, defended models may react differently to variations of this perturbation size.
In this paper, the analogy of domain adaptation is taken a step further by exploiting optimal transport theory. We propose to use a loss between distributions that faithfully reflect the ground distance. This leads to SAT (Sinkhorn Adversarial Training), a more robust defense against adversarial attacks.
Then, we propose to quantify more precisely the robustness of a model to adversarial attacks over a wide range of perturbation sizes using a different metric, the Area Under the Accuracy Curve (AUAC). We perform extensive experiments on both CIFAR-10 and CIFAR-100 datasets and show that our defense is globally more robust than the state-of-the-art.

Optimal Transport As a Defense against Adversarial Attacks

Analysis of longitudinal changes in brain diseases is essential for a better characterization of pathological processes and evaluation of the prognosis. This is particularly important in Multiple Sclerosis (MS) which is the first traumatic disease in young adults, with unknown etiology and characterized by complex inflammatory and degenerative processes leading to different clinical courses. In this work, we propose a fully automated tensor-based algorithm for the classification of MS clinical forms based on the structural connectivity graph of the white matter (WM) network. Using non-negative tensor factorization (NTF), we first focused on the detection of pathological patterns of the brain WM network affected by significant longitudinal variations. Second, we performed unsupervised classification of different MS phenotypes based on these longitudinal patterns, and finally, we used the latent factors obtained by the factorization algorithm to identify the most affected brain regions.

Tensor Factorization of Brain Structural Graph for Unsupervised Classification in Multiple Sclerosis


In this talk we present a novel defogging technique, named CurL-Defog, aimed at minimizing the creation of unwanted artifacts during the defogging process. The majority of learning based defogging approaches rely on paired data (i.e., the same images with and without fog), where fog is artificially added to clear images: this often provides good results on mildly fogged images but does not generalize well to real difficult cases. On the other hand, the models trained with real unpaired data (e.g. CycleGAN) can provide visually impressive results but they often produce unwanted artifacts. In this paper we propose a curriculum learning strategy coupled with an enhanced CycleGAN model in order to reduce the number of produced artifacts, while maintaining state-of-the-art performance in terms of contrast enhancement and image reconstruction. We also introduce a new metric, called HArD (Hazy Artifact Detector) to numerically quantify the amount of artifacts in the defogged images, thus avoiding the tedious and subjective manual inspection of the results. The proposed approach compares favorably with state-of-the-art techniques on both real and synthetic datasets.


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Hierarchical Head Design for Object Detectors

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