Italy

For image ranking tasks with naturally continuous output, such as age and scenicness estimation, it is common to discretize the label range and apply methods from (ordered)classification analysis. In this paper, we propose a data-driven approach for simultaneous representation learning and label discretization. Compared to arbitrarily selecting thresholds, we seek to learn thresholds and image representations by minimizing a novel loss function in an end-to-end model. We demonstrate our combined approach on a variety of image ranking tasks and demonstrate that it outperforms task-specific methods. Additionally, our learned partitioning scheme can be transferred to improve methods that rely on discretization.

ICPR 2020

Learning Natural Thresholds for Image Ranking

logistic regression

label discretization

classification

**BRACIS** originated from the combination of the two most important scientific events in Brazil in Artificial Intelligence (AI) and Computational Intelligence (CI): the Brazilian Symposium on Artificial Intelligence – **SBIA** (22 editions), and the Brazilian Symposium on Neural Networks – **SBRN** (13 editions). **ENIAC** is a national forum for researchers, practitioners, educators and students to present and discuss innovations, trends, experiences, developments and work in progress in the fields of Artificial and Computational Intelligence. **STIL** is the bi-annual Language Technology event.
All events are supported by the Brazilian Computer Society – Special Interest Groups: **CE-IA**, **CE-IC** and **CE-PLN**.

To access the 10th Brazilian Conference on Intelligent Systems (**BRACIS**), you need to register for the conference.  Please visit the Conference official site to register

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BRACIS 2021

BRACIS originated from the combination of the two most important scientific events in Brazil in Artificial Intelligence (AI) and Computational Intelligence (CI): the Brazilian Symposium on Artificial Intelligence – SBIA (22 editions), and the Brazilian Symposium on Neural Networks – SBRN (13 editions). 
ENIAC is a national forum for researchers, practitioners, educators and students to present and discuss innovations, trends, experiences, developments and work in progress in the fields of Artificial and Computational Intelligence. 
STIL is the bi-annual Language Technology event.
All events are supported by the Brazilian Computer Society – Special Interest Groups: CE-IA, CE-IC and CE-PLN

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. 



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.

Revisiting GNN: Graph Filtering Perspective

GAP: Quantifying the Generative Adversarial Set and Class Feature Applicability of Deep Neural Networks

Pedestrian lane detection is a core component in many assistive and autonomous navigation systems. These systems are usually deployed in environments that require real-time processing. Many state-of-the-art deep neural networks only focus on detection accuracy but not inference speed. Without further modifications, they are not suitable for real-time applications. Furthermore, the task of designing a high-performing deep neural network is time-consuming and requires experience. To tackle these issues, we propose a neural architecture search algorithm that can find the best deep network for pedestrian lane detection automatically. The proposed method searches in a network-level space using the gradient descent algorithm. Evaluated on a dataset of 5,000 images, the deep network found by the proposed algorithm achieves comparable segmentation accuracy, while being significantly faster than other state-of-the-art methods. The proposed method has been successfully implemented as a real-time pedestrian lane detection tool.

Real-time Pedestrian Lane Detection for Assistive Navigation using Neural Architecture Search

Intersection scenarios provide the most complex trafﬁc situations in Autonomous Driving and Driving Assistance Systems. Knowing where to stop in advance in an intersection is an essential parameter in controlling the longitudinal velocity of the vehicle. Most of the existing methods in literature solely use cameras to detect stop lines, which is typically not sufﬁcient in terms of detection range. To address this issue, we propose a method that takes advantage of fused multi-sensory data including stereo camera and lidar as input and utilizes a carefully designed convolutional neural network architecture to detect stop lines. Our experiments show that the proposed approach can improve detection range compared to camera data alone, works under heavy occlusion without observing the ground markings explicitly, is able to predict stop lines for all lanes and allows detection at a distance up to 50 meters.

Holistic Grid Fusion Based Stop Line Estimation


Modern Convolutional Neural Networks~(CNNs) have been improving the accuracy of person re-identification (re-id) using a large number of training samples.
Such a re-id system suffers from a lack of training samples for deployment to practical security applications. To address this problem, we focus on the approach that transfers features of a CNN pre-trained on a large-scale person re-id dataset to a small-scale dataset. Most of the existing CNN feature transfer methods use the features of fully connected layers that entangle locally pooled features of different spatial locations on an image. 
Unfortunately, due to the difference of view angles and the bias of walking directions of the persons, each camera view in a dataset has a unique spatial property in the person image, which reduces the generality of the local pooling for different cameras/datasets. 
To account for the camera- and dataset-specific spatial bias, we propose a method to learn camera and dataset-specific position weight maps for discriminative local pooling of convolutional features. 
Our experiments on four public datasets confirm the effectiveness of the proposed feature transfer with a small number of training samples in the target datasets.


Convolutional Feature Transfer via Camera-Specific Discriminative Pooling for Person Re-Identification

We present a Multi-Features Siamese Tracker (MFST), a novel tracking algorithm exploiting several hierarchical feature maps for robust deep similarity tracking. Since Convolutional layers provide several abstraction levels in characterizing an object, fusing hierarchical features allows to ensure a richer and more efficient representation. Moreover, we handle the target appearance variation by calibrating deep features extracted from two different CNN models. Based on this advanced feature representation, our method achieves high tracking accuracy, while outperforming standard Siamese trackers on object tracking benchmarks.

MFST: Multi-Features Siamese Tracker

We propose an approach for explicitly capturing context in object detection. We model visual and geometric relationships between object regions, but also model the global scene as a first-class participant. In contrast to prior approaches, both the context we rely on, as well as our proposed mechanism for belief propagation over regions, are lightweight. We also experiment with capturing similarities between regions at a semantic level, by modeling class co-occurrence and linguistic similarity between class names. We show that our approach significantly outperforms Faster R-CNN, and performs competitively with a much more costly approach that also models context.

Context for Object Detection via Lightweight Global and Mid-level Representations


we propose a novel framework for multi-image co-segmentation using class agnostic meta-learning strategy by generalizing to new classes given only a small number of training samples for each new class. We have developed a novel encoder-decoder network termed as DVICE (Directed Variational Inference Cross Encoder), which learns a continuous embedding space to ensure better similarity learning. We employ a combination of the proposed DVICE network and a novel few-shot learning approach to tackle the small sample size problem encountered in co-segmentation with small datasets like iCoseg and MSRC. Furthermore, the proposed framework does not use any semantic class labels and is entirely class agnostic. Through exhaustive experimentation over multiple datasets using only a small volume of training data, we have demonstrated that our approach outperforms all existing state-of-the-art techniques.

Directed Variational Cross-encoder Network for Few-shot Multi-image 
Co-segmentation

What nodes vote to? Graph classification without readout phase

In this presentation, we propose a novel framework called “deep photo relighting” (DPR) that can transform the lighting condition of an image for a virtual test of image detection/classification algorithm, city environment design, and data augmentation for machine learning. DPR has two keypoints for transforming one lighting condition to another one by DNN. One is that we can support all factors that affect the lighting conditions by using 2D and 3D information. The other keypoint is that we can reproduce indirect influences from outside the frame. As a result, DPR can generate relighting image without fatal artifacts such an unnatural shading/shadows of objects. In experiments, we confirmed that a generated image is well reproduced compared with the ground truth image. We also confirmed that shadows, which occur inside and outside the frame through obstacles, are properly added/deleted in the generated image compared with the ground truth image.

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Learning Natural Thresholds for Image Ranking

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