Italy

3D object reconstruction from a single-view image is a long-standing challenging problem. Previous work was difficult to accurately reconstruct 3D shapes with a complex topology which has rich details at the edges and corners. Moreover,
previous works used synthetic data to train their network, but domain adaptation problems occurred when tested on real data. In this paper, we propose a Dynamic Multi-branch Information Fusion Network (DmifNet) which can recover a high-fidelity 3D shape of arbitrary topology from a 2D image. Specifically, we design several side branches from the intermediate layers to make the network produce more diverse representations to improve the generalization ability of network. In addition, we utilize DoG (Difference of Gaussians) to extract edge geometry and corners information from input images. Then, we use a separate side branch network to process the extracted data to better capture edge geometry and corners feature information. Finally, we dynamically fuse the information of all branches to gain final predicted probability. Extensive qualitative and quantitative experiments on a large-scale publicly available dataset demonstrate the validity and efficiency of our method. Code and models are publicly available at https://github.com/leilimaster/DmifNet.


ICPR 2020

DmifNet: 3D Shape Reconstruction Based on Dynamic Multi-Branch Information Fusion

dynamic information fusion

difference of gaussians

multi-branch network

3d reconstruction

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. 


technical paper

In this paper, we propose a multi-attribute regression network (MARN) to investigate the problem of face reconstruction, especially in challenging cases when faces undergo large variations including severe poses, extreme expressions, and partial occlusions in unconstrained environments. The traditional 3DMM parametric regression method does not distinguish the
learning of identity, expression, and attitude attributes, resulting in lacking geometric details in the reconstructed face. We propose to learn a face multi-attribute features during 3D face reconstruction from single 2D images. Our MARN enables the network to better extract the feature information of face identity, expression, and pose attributes. We introduce three loss functions
to constrain the above three face attributes respectively. At the same time, we carefully design the geometric contour constraint loss function, using the constraints of sparse 2D face landmarks to improve the reconstructed geometric contour information.The experimental results show that our MARN has achieved
significant improvements in 3D face reconstruction and face alignment on the AFLW2000-3D and AFLW datasets.

Multi-Attribute Regression Network for Face Reconstruction

Automatic recognition of products on grocery shelf images is a new and attractive topic in computer vision and machine learning since, it can be exploited in different application areas. This paper introduces a complete end-to-end pipeline,
without preliminary radiometric and spatial transformations usually involved while dealing with the considered issue, and it provides a systematic investigation of recent machine learning models based on convolutional neural networks for addressing the product recognition task by exploiting the proposed pipeline
on a recent challenging grocery product dataset. The investigated models were never been used in this context: they derive from the successful and more generic object recognition task and have been properly tuned to address this specific issue. Besides, also ensembles of nets built by the most advanced theoretical fundaments have been taken into account. Gathered classification results were very encouraging since the recognition accuracy has been improved up to 15% with respect to the leading approaches in the state of art on the same dataset. A discussion about the pros and cons of the investigated solutions are discussed by paving the path towards new research lines.

A Systematic Investigation on end-to-end DeepRecognition of Grocery Products in the Wild

The integration of rich sensory technologies into critical applications, such as gesture recognition and building energy optimization, has highlighted the importance of intelligent time-series analytics. To accommodate this demand, uni-variate approaches have been extended for multi-variate scenarios, but naive extensions have led to a deterioration in model performances due to their limited ability to capture the information recorded in different variates and complex multi-variate time series patterns’ evolution over time. Furthermore, real-world time series are often contaminated with noisy information. In this paper, we note that a time series often carries robust localized temporal events that could help improve model performance by highlighting the relevant information; however, the lack of sufficient data to train for these events makes it impossible for neural architectures to identify and make use of these temporal events. We, therefore, argue that a companion process helping identify salient events in the input time series and driving the model’s attention to the associated salient sub-sequences can help with learning a high-performing network. Relying on this observation, we propose a novel Saliency-Driven Mutual Cross Attention (SDMA) framework that extracts localized temporal events and generates a saliency series to complement the input time series. We further propose an architecture that accounts for the mutual cross-talk between the input and saliency series branches where input and saliency series attend to each other. Experiments show that the proposed mutually-cross attention framework can offer significant boosts in model performance when compared against non-attentioned, conventionally attentioned, and conventionally cross-attentioned models.

SDMA: Saliency-Driven Mutual Cross Attention for Multi-Variate Time Series


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.

Soft Label and Discriminant Embedding Estimation for Semi-Supervised Classification

The main goal of discriminant embedding is to extract features that can be compact and informative representations of the original set of features. This paper introduces a hybrid scheme for linear feature extraction for supervised multiclass
classification. We introduce a unifying criterion that is able to retain the advantages of robust sparse LDA and Interclass sparsity. Thus, the estimated transformation includes two types of discrimination which are the inter-class sparsity and
robust Linear Discriminant Analysis with feature selection. In order to optimize the proposed objective function, we deploy an iterative alternating minimization scheme for estimating the linear transformation and the orthogonal matrix.
The introduced scheme is generic in the sense that it can be used for combining and tuning many other linear embedding methods. In the lights of the experiments conducted on six image datasets including faces, objects, and digits, the proposed scheme was able to outperform competing methods in most of the cases.

Feature Extraction and Selection via Robust Discriminant Analysis and Class Sparsity

This paper explores the process of designing an automatic multi-sensor drone detection system. Besides the common video and audio sensors, the system also includes a thermal infrared camera, which is shown to be a feasible solution to the drone detection task. Even with slightly lower resolution, the performance is just as good as a camera in visible range. The detector performance as a function of the sensor-to-target distance is also investigated. In addition, using sensor fusion, the system is made more robust than the individual sensors, helping to reduce false detections. To counteract the lack of public datasets, a novel video dataset containing 650 annotated infrared and visible videos of drones, birds, airplanes and helicopters is also presented. The database is complemented with an audio dataset of the classes drones, helicopters and background noise.

Real-Time Drone Detection and Tracking With Visible, Thermal and Acoustic Sensors

VINNAS: Variational inference-based neural network architecture search

Identity-preserved face beauty transformation aims to change the beauty scale of a face image while preserving the identity of the original face. In our framework of conditional Generative Adversarial Networks (cGANs), the synthesized face produced by the generator would have the same beauty scale indicated by the input condition. Unlike the discrete class labels used in most cGANs, the condition of target beauty scale in our framework is given by a continuous real-valued beauty score in the range [1 to 5], which makes the work challenging. To tackle the problem, we have implemented a triple structure, in which the conditional discriminator is divided into a normal discriminator and a separate face beauty predictor. We have also developed another new structure called Conditioned Instance Normalization to replace the original concatenation used in cGANs, which makes the combination of the input image and condition more effective. Furthermore, Self-Consistency Loss is introduced as a new parameter to improve the stability of training and quality of the generated image. In the end, the objectives of beauty transformation and identity preservation are evaluated by the pretrained face beauty predictor and state-of-the-art face recognition network. The result is encouraging and it also shows that certain facial features could be synthesized by the generator according to the target beauty scale, while preserving the original identity.

Identity-Preserved Beauty Transformation with cGANs

Generation of high density depth values of the driving environment is indispensable for autonomous driving. Stereo vision is one of the practical and effective methods to generate these depth values. However, the accuracy of the stereo vision is
limited by texture-less regions, such as sky and road areas, and repeated patterns in the image. To overcome these problems, we propose to enhance the stereo generated depth by incorporating prior information of the driving environment. Prior information, generated by deep learning-based U-Net model, is utilized in
a novel post-processing mathematical framework to refine the stereo generated depth. The proposed mathematical framework is formulated as an optimization problem, which refines the errors due to texture-less regions and repeated patterns. Owing to its mathematical formulation, the post-processing framework
is not a black-box and is explainable, and can be readily utilized for depth maps generated by any stereo vision algorithm. The proposed framework is qualitatively validated on the acquired dataset and KITTI dataset. The results obtained show that
the proposed framework improves the stereo depth generation accuracy

Enhancing Depth Quality of Stereo Vision using Deep Learning-based Prior Information of the Driving Environment

Financial time-series analysis and forecasting have been extensively studied over the past decades, yet still remain as a very challenging research topic. Since the financial market is inherently noisy and stochastic, a majority of financial time-series of interests are non-stationary, and often obtained from different modalities. This property presents great challenges and can significantly affect the performance of the subsequent analysis/forecasting steps. Recently, the Temporal Attention augmented Bilinear Layer (TABL) has shown great performances in tackling financial forecasting problems. In this paper, by taking into account the nature of bilinear projections in TABL networks, we propose Bilinear Normalization (BiN), a simple, yet efficient normalization layer to be incorporated into TABL networks to tackle potential problems posed by non-stationarity and multimodalities in the input series. Our experiments using a large scale Limit Order Book (LOB) consisting of more than 4 million order events show that BiN-TABL outperforms TABL networks using other state-of-the-arts normalization schemes by a large margin.

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