Italy

3D points distribute in a continuous 3D space irregularly, thus directly adapting 2D image convolution to 3D points is not an easy job. Our model can naturally generalizes image CNN to deal with point clouds. The key idea of our model is learning to probe the 3D space in an adaptive manner. Experiments across three tasks demonstrate that our model achieves the state-of-the-art or competitive performance.

ICPR 2020

Deep Space Probing for Point Cloud Analysis

gpu memory

convolution

point cloud

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


In the field of multimodal segmentation, the correlation between different modalities can be considered for improving the segmentation results. In this paper, we propose a multi-modality segmentation network with a correlation constraint. Our network includes N model-independent encoding paths with N image sources, a correlation constraint block, a feature fusion block, and a decoding path. The model independent encoding path can capture modality-specific features from the N modalities. Since there exists a strong correlation between different modalities, we first propose a linear correlation block to learn the correlation between modalities, then a loss function is used to guide the network to learn the correlated features based on the linear correlation block. This block forces the network to learn the latent correlated features which are more relevant for segmentation. Considering that not all the features extracted from the encoders are useful for segmentation, we propose to use dual attention based fusion block to recalibrate the features along the modality and spatial paths, which can suppress less informative features and emphasize the useful ones. The fused feature representation is finally projected by the decoder to obtain the segmentation result. Our experiment results tested on BraTS-2018 dataset for brain tumor segmentation demonstrate the effectiveness of our proposed method.

3D Medical Multi-modal Segmentation Network Guided by Multi-source Correlation Constraint

For full process automation in Industry 4.0 with smart machines, localizing objects is a crucial capability. Enabling such an object localization with Machine Learning is tempting, as it avoids a manual engineering of features for every object class, yet gathering large amounts of accurate pose data to train neural networks can be just as difficult.

In this work, a novel self-supervised approach based on point clouds is presented, which resolves these issues and allows a robust detection and localization of objects in cluttered scenes, while compensating for noise, occlusions and symmetries. It leverages 3D sensor data simulation being simpler than light rendering to construct random scenes using a stochastic process by maintaining object relations with placement operations like cloning, stacking and storing, and to then train a fully convolutional voting network with random scans from those scenes.

Self-Supervised Detection and Pose Estimation of Logistical Objects in 3D Sensor Data

In this paper, a novel method for blending planar shapes is introduced. This approach is based on the Fined-Fourier-based Invariant Descriptor (Fined-FID) that is invertible, invariant under Euclidean transformations and stable. Our approach extracts the Fined-FID from the two shapes of interest (the source and the target ones). Then, the extracted descriptors are averaged enabling the calculation of intermediate descriptors. Finally, thanks to the inversion criterion, the intermediate shapes are easily recovered by applying the inverse analytical expression to these intermediate descriptors. Compared to previous works, the Fined-FID-based morphing avoid the usual registration step, generates naturally closed intermediate contours and ensure invariance under Euclidean transformations and invariance to the starting point, while being computationally efficient (almost-linear complexity).
The performed experiments show the performance of the proposed blending approach with respect to curvature-based methods.

Fast blending of planar shapes based on invariant invertible and stable descriptors


We propose Spherical Hierarchical modeling of 3D point cloud.  Inspired by Shape Context, we design a receptive field on each 3D point by placing a spherical coordinate on it. We sample points using the furthest point method and creating overlapping balls of points. We divide the space into radial, polar angular, and azimuthal angular bins on which we form a Spherical Hierarchy for each ball. We apply 1x1 CNN convolution on points to start the initial feature extraction. Repeated 3D CNN and max-pooling over the Spherical bins propagate contextual information until all the information is condensed in the center bin. Extensive experiments on five datasets strongly evidence that our method outperforms current models on various Point Cloud Learning tasks, including 2D/3D shape classification, 3D part segmentation, and 3D semantic segmentation.

PointSpherical: Deep Shape Context for Point Cloud Learning in Spherical Coordinates


Point clouds generated from real-world scanning are always incomplete and ununiformly distributed, which would cause structural losses in 3D shape representations. Therefore, a learning-based method is introduced in this paper to repair partial point clouds and restore complete shapes of target objects. First, we sample several local regions of inputs, encode their features and fuse them with independently extracted global features. Second, we establish a graph to connect all local features together, and then implement convolution with multi-head attention on the graph. Graph attention mechanism enables each local feature vector to search across the regions and selectively absorb other local features based on their relationships in high-dimensional feature space. Third, we design a coarse decoder to collect cross-region features from the graph and generate skeletons of complete point clouds, and a folding-based decoder is leveraged to generate final point clouds with high resolution. Our network is trained on six categories of objects from the ModelNet dataset, its performance is compared with several existing methods, the results show that our network is able to generate dense complete point cloud with the highest accuracy.

Cross-Regional Attention Network for Point Cloud Completion


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.

Multi-annotator Probabilistic Active Learning

We propose Zoom-CAM visual explanations for generating pixel-level pseudo-labels from class labels for weakly supervised object localization and segmentation tasks. Zoom-CAM captures fine-grained small-scale objects for various discriminative class instances by integrating the visualizations from all intermediate convolutional layers in a CNN.

Zoom-CAM: Generating Fine-Grained Pixel Annotations from Image Labels

We present two methods to reduce the complexity of Bayesian network (BN) classifiers. First, we introduce quantization-aware training using the straight-through gradient estimator to quantize the parameters of BNs to few bits. Second, we extend a recently proposed differentiable tree-augmented naive Bayes (TAN) structure learning approach by also considering the model size. Both methods are motivated by recent developments in the deep learning community, and they provide effective means to trade off between model size and prediction accuracy, which is demonstrated in extensive experiments. Furthermore, we contrast quantized BN classifiers with quantized deep neural networks (DNNs) for small-scale scenarios which have hardly been investigated in the literature. We show Pareto optimal models with respect to model size, number of operations, and test error and find that both model classes are viable options.

On Resource-Efficient Bayesian Network Classifiers and Deep Neural Networks

Weakly Supervised Object Localization (WSOL) task aims to recognize the object position by using only image-level labels. Some previous techniques remove the most discriminative parts for all input images or random images to capture the entire object location. However, these methods can not perform the correct operation on different images such as hiding the data or feature maps that should not be hidden. In this case, both classification and localization accuracy will be affected. Meanwhile, just erasing the most important regions tends to make the model learn the less discriminative parts from outside of the objects. To address these limitations, we propose an Attention-based Selection Strategy (ASS) method to choose images that do need to be erased. Moreover, we use different threshold self-attention maps to reduce the impact of unhelpful information in one of the branches of our selection strategy. Based on our experiments, the proposed method is simple but effective to improve the performance of WSOL. In particular, ASS achieves new state-of-the-art accuracy on CUB-200-2011 dataset and works very well on ILSVRC 2016 dataset.

Attention-based Selection Strategy for Weakly Supervised Object Localization


Tracking Fast Moving Objects (FMO), which appear
as blurred streaks in video sequences, is a difficult task for
standard trackers, as the object position does not overlap in
consecutive video frames and texture information of the objects
is blurred. Up-to-date approaches tuned for this task are based
on background subtraction with a static background and slow
deblurring algorithms. In this article, we present a tracking-by-segmentation
approach implemented using modern deep learning
methods that perform near real-time tracking on real-world
video sequences. We have developed a physically plausible FMO
sequence generator to be a robust foundation for our training
pipeline and demonstrate straightforward network adaptation for
different FMO scenarios with varying foreground.

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