Italy

Point clouds have emerged as a popular representation of 3D visual data. With a set of unordered 3D points, one typically needs to transform them into latent representation before further classification and segmentation tasks. However, one cannot easily interpret such encoded latent representation. To address this issue, we propose a unique deep learning framework for disentangling body-type and pose information from 3D point clouds. Extending from autoencoder, we advance adversarial learning a selected feature type, while classification and data recovery can be additionally observed. Our experiments confirm that our model can be successfully applied to perform a wide range of 3D applications like shape synthesis, action translation, shape/action interpolation, and synchronization.

ICPR 2020

Learning Interpretable Representation for 3D Point Clouds

interpretable representation

disentanglement

point clouds

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


SigNet is a state of the art model for feature representation used for handwritten signature verification (HSV). This representation is based on a Deep Convolutional Neural Network (DCNN) and contains 2048 dimensions. When transposed to a dissimilarity space generated by the dichotomy transformation (DT), related to the writer-independent (WI) approach, these features may include redundant information. This paper investigates the presence of overfitting when using Binary Particle Swarm Optimization (BPSO) to perform the feature selection in a wrapper mode. We proposed a method based on a global validation strategy with an external archive to control overfitting during the search for the most discriminant representation. Moreover, an investigation is also carried out to evaluate the use of the selected features in a transfer learning context. The analysis is carried out on a writer-independent approach on the CEDAR, MCYT and GPDS datasets. The experimental results showed the presence of overfitting when no validation is used during the optimization process and the improvement when the global validation strategy with an external archive is used. Also, the space generated after feature selection can be used in a transfer learning context.

An Investigation of Feature Selection and Transfer Learning for Writer-Independent Offline Handwritten Signature Verification

This paper addresses the problem of decentralized learning
to achieve a high-performance global model by asking a
group of clients to share local models pre-trained with
their own data resources. We are particularly interested in
a specific case where both the client model architectures
and data distributions are diverse, which makes it
nontrivial to adopt conventional approaches such as
Federated Learning and network co-distillation. To this
end, we propose a new decentralized learning method called
Decentralized Learning via Adaptive Distillation (DLAD).
Given a collection of client models and a large number of
unlabeled distillation samples, the proposed DLAD 1)
aggregates the outputs of the client models while
adaptively emphasizing those with higher confidence in
given distillation samples and 2) trains the global model
to imitate the aggregated outputs. Our extensive
experimental evaluation on multiple public datasets (MNIST,
CIFAR-10, and CINIC-10) demonstrates the effectiveness of
the proposed method.

Adaptive Distillation for Decentralized Learning from Heterogeneous Clients


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.

TreeRNN: Topology-Preserving Deep Graph Embedding and Learning

We consider the problem of uncovering an unknown
attributed graph, where both its edges and vertices are hidden
from view, through a sequence of binary questions about it.
In order to select questions efficiently, we define a probability
distribution over graphs, with randomness not just over edges,
but over vertices as well. We then sequentially select questions so
as to: (1) minimize the expected entropy of the random graph,
given the answers to the previous questions in the sequence; and
(2) instantiate the vertices that compose the graph. We propose
some basic question spaces, from which to select questions, that
vary in their capacity.

We apply this framework to the problem of test generation in
Visual Question Answering (VQA), where semantic questions are
used to evaluate vision systems over rich image representations.
To do this, we use a restricted question vocabulary, resulting
in image representations that take the form of scene graphs; by
defining a distribution over them, a consistent set of probabilities
is associated with the questions, and used in their selection.

Graph Discovery for Visual Test Generation


This paper targets to explore the inter-subject variations eliminated facial expression representation in the compressed video domain. Most of the previous methods process the RGB images of a sequence, while the off-the-shelf and valuable expression-related muscle movement already embedded in the compression format. In the up to two orders of magnitude compressed domain, we can explicitly infer the expression from the residual frames and possible to extract identity factors from the I frame with a pre-trained face recognition network. By enforcing the marginal independent of them, the expression feature is expected to be purer for the expression and be robust to identity shifts. We do not need the identity label or multiple expression samples from the same person for identity elimination. Moreover, when the apex frame is annotated in the dataset, the complementary constraint can be further added to regularize the feature-level game. In testing, only the compressed residual frames are required to achieve expression prediction. Our solution can achieve comparable or better performance than the recent decoded image based methods on the typical FER benchmarks with about 3$\times$ faster inference with compressed data.

Identity-aware Facial Expression Recognition in Compressed Video

Energy-constrained Self-training for Unsupervised Domain Adaptation

The unrestricted open vocabulary and diverse substances of scenery images bring significant challenges to scene recognition. However, most deep learning architectures and attention methods are developed on general-purpose datasets and omit the characteristics of scene data. In this paper, we exploit the attention pyramid module (APM) to tackle the predicament of scene recognition. Our method streamlines the multi-scale scene recognition pipeline, learns comprehensive scene features at various scales and locations, addresses the interdependency among scales, and further assists feature re-calibration as well as aggregation process. APM is extremely light-weighted and can be easily plugged into existing network architectures in a parameter efficient manner. By simply integrating APM into ResNet-50, we obtain a 3.54% boost in terms of top-1 accuracy on the benchmark scene dataset. Comprehensive experiments show that APM achieves better performance comparing with state-of-the-art attention methods using significantly less computation budget.

Attention Pyramid Module for Scene Recognition


In this work, we propose a motion embedding strategy known as motion codes, which is a vectorized representation of motions based on a manipulation's salient mechanical attributes. These motion codes provide a robust motion representation, and they are obtained using a hierarchy of features called the motion taxonomy. We developed and trained a deep neural network model that combines visual and semantic features to identify the features found in our motion taxonomy to embed or annotate videos with motion codes. To demonstrate the potential of motion codes as features for machine learning tasks, we integrated the extracted features from the motion embedding model into the current state-of-the-art action recognition model. The obtained model achieved higher accuracy than the baseline model for the verb classification task on egocentric videos from the EPIC-KITCHENS dataset.

Developing Motion Code Embedding for Action Recognition in Videos


Recently, facial priors based face super-resolution (SR) methods have obtained significant performance gains in dealing with extremely degraded facial images, and facial priors have also been proved useful in facilitating the inference of face images. Based on this, how to efficiently fuse facial priors into deep features to improve face SR performance has attracted a major attention. In this paper, we propose a learning-based face SR approach with Incremental boosting Parsing information (dubbed IPFSR) for high magnification factor. The proposed IPFSR method consists of three main parts: i) a multi-stage parsing map embedded features upsampling network, in which image recovery and prior estimation processes are performed simultaneously and progressively to improve image resolution; ii) a progressive training method and joint facial attention loss and heatmap loss to obtain better facial attributes; iii) the channel attention strategy is introduced into residual dense blocks to adaptively learn facial features. Extensive experimental results show that compared with the state-of-the-art methods in terms of quantitative and qualitative metrics, our approach can achieve an outstanding balance between SR image quality and low network complexity.

Face Super-Resolution Network with Incremental Enhancement of Facial Parsing Information

In this work, we introduce a generalization methodology for the automatic selection of the activation functions inside a neural network, taking advantage of concepts defined in fractional calculus. This methodology enables the neural network to search and optimize its own activation functions during the training process, by defining the fractional order of the derivative of a given primitive activation function. This fractional order is tuned as an additional training hyper-parameter $a$ for intrafamily selection and $b$ for cross family selection. By following this approach, the neurons inside the network can adjust their activation functions, e.g. from MLP to RBF networks, to best fit the input data, and reduce the output error. The experimental results obtained show the benefits of using this technique implemented on a ResNet18 topology, by outperforming the accuracy of a ResNet100 trained with CIFAR10 and Improving 1% ImageNet reported in the literature.

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Learning Interpretable Representation for 3D Point Clouds

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