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Multi-view learning is a learning task in which data is described by several concurrent representations. Its main challenge is most often to exploit the complementarities between these representations to help solve a classification/regression task. This is a challenge that can be met nowadays if there is a large amount of data available for learning. However, this is not necessarily true for all real-world problems, where data are sometimes scarce (e.g. problems related to the medical environment). In these situations, an effective strategy is to use intermediate representations based on the dissimilarities between instances. This work presents new ways of constructing these dissimilarity representations, learning them from data with Random Forest classifiers. More precisely, two methods are proposed, which modify the Random Forest proximity measure, to adapt it to the context of High Dimension Low Sample Size (HDLSS) multi-view classification problems. The second method, based on an Instance Hardness measurement, is significantly more accurate than other state-of-the-art measurements including the original RF Proximity measurement and the Large Margin Nearest Neighbor (LMNN) metric learning measurement.

ICPR 2020

A Novel Random Forest Dissimilarity Measure for Multi-View Learning

high dimension low sample size

random forest

dissimilarity representation

multi-view learning

AIAI 2020 (16th International Conference on Artificial Intelligence Applications and Innovations) should be held in Halkidiki, Greece. Due to current Covid-19 situation the conference will be held virtually. Underline will host a repository of video talks and preserve them for the future. Videos will be released on Underline one day after the conference.

AIAI 2020

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. 


Human action recognition is one of the challenging and active research fields due to its wide applications. Recently, graph convolutions for skeleton-based action recognition have attracted much attention. Generally, the adjacency matrices of the graph are fixed to the hand-crafted physical connectivity of the human joints, or learned adaptively via deep learning. The hand-crafted or learned adjacency matrices are fixed when processing each frame of an action sequence. However, the interactions of different subsets of joints may play a core role at different phases of an action. Therefore, it is reasonable to evolve the graph topology with time. In this paper, a recurrent graph convolution is proposed, in which the graph topology is evolved via a long short-term memory (LSTM) network. The proposed recurrent graph convolutional network (R-GCN) can recurrently learn the data-dependent graph topologies for different layers, different time steps and different kinds of actions. Experimental results on the NTU RGB+D and Kinetics-Skeleton datasets demonstrate the advantages of the proposed R-GCN.

Recurrent Graph Convolutional Networks for Skeleton-based Action Recognitions

Feature selection has become a crucial part of many classification problems in which high-dimensional datasets may contain tens of thousands of features. In this paper, we propose a novel feature selection method scoring the features through
estimating the Bayes error rate based on kernel density estimation. Additionally, we update the scores of features dynamically by quantitatively interpreting the effects of feature relevance and redundancy in a new way. Distinguishing from the common
heuristic applied by many feature selection methods, which prefers choosing features that are not relevant to each other, our approach penalizes only monotonically correlated features and rewards any other kind of relevance among features based on Spearman's rank correlation coefficient and normalized mutual information. We conduct extensive experiments on seventeen diverse classification benchmarks and the results show that our approach overperforms other seventeen popular state-of-the-art feature selection methods in most cases.

Watermelon: a Novel Feature Selection Method Based on Bayes Error Rate Estimation and a New Interpretation of Feature Relevance and Redundancy

We propose a Stable Age Translation GAN (SATGAN) to generate fake face images at different ages to augment age biased face datasets for Cross-Age Face Recognition (CAFR) . The proposed SATGAN consists of both generator and discriminator. As a part of the generator, a novel Mask Attention Module (MAM) is introduced to make the generator focus on the face area. In addition, the generator employs a Uniform Distribution Discriminator (UDD) to supervise the learning of latent feature map and enforce the uniform distribution. Besides, the discriminator employs a Feature Separation Module (FSM) to disentangle identity information from the age information.

SATGAN: Augmenting Age Biased Dataset for Cross-Age Face Recognition

Deep neural networks (DNNs) have recently been applied and used in many advanced and diverse tasks, such as medical diagnosis, automatic driving, etc. Due to the lack of transparency of the deep models, DNNs are often criticized for their prediction that cannot be explainable by human.
In this paper, we propose a novel Morphological Fragmental Perturbation Pyramid (MFPP) method to solve the Explainable AI problem. In particular, we focus on the black-box scheme, which can identify the input area that is responsible for the output of the DNN without having to understand the internal architecture of the DNN. In the MFPP method, we divide the input image into multi-scale fragments and randomly mask out fragments as perturbation to generate a saliency map, which indicates the significance of each pixel for the prediction result of the black box model. Compared with the existing input sampling perturbation method, the pyramid structure fragment has proved to be more effective. It can better explore the morphological information of the input image to match its semantic information, and does not need any value inside the DNN. We qualitatively and quantitatively prove that MFPP meets and exceeds the performance of state-of-the-art (SOTA) black-box interpretation method on multiple DNN models and datasets.

MFPP: Morphological Fragmental Perturbation Pyramid for Black-Box Model Explanations

We present a novel deep zero-shot learning (ZSL) model for inferencing human-object-interaction with verb-object (VO) query. While the previous two-stream ZSL approaches only use the semantic/textual information to be fed into the query stream, we seek to incorporate and embed the semantics into the visual representation stream as well. Our approach is powered by Semantics-to-Space (S2S) architecture where semantics derived from the residing objects are embedded into a spatial space of the visual stream. This architecture allows the co-capturing of the semantic attributes of the human and the objects along with their location/size/silhouette information. To validate, we have constructed a new dataset, Verb-Transferability 60 (VT60). VT60 provides 60 different VO pairs with overlapping verbs tailored for testing two-stream ZSL approaches with VO query. Experimental evaluations show that our approach not only outperforms the state-of-the-art, but also shows the capability of consistently improving performance regardless of which ZSL baseline architecture is used.

Semantics to Space(S2S): Embedding semantics into  spatial space for zero-shot verb-object query inferencing

Recent advances in reconstruction methods for inverse problems leverage powerful data-driven models, e.g., deep neural networks. These techniques have demonstrated state-of-the-art performances for several imaging tasks, but they often do not provide uncertainty on the obtained reconstruction. In this work, we develop a scalable, data-driven, knowledge-aided computational framework to quantify the model uncertainty via Bayesian neural networks. The approach builds on, and extends deep gradient descent, a recently developed greedy iterative training scheme, and recasts it within a probabilistic framework. Scalability is achieved by being hybrid in the architecture: only the last layer of each block is Bayesian, while the others remain deterministic, and by being greedy in training. The framework is showcased on one representative medical imaging modality, viz. computed tomography with either sparse view or limited view data, and exhibits competitive performance with respect to state-of-the-art benchmarks, e.g., total variation, deep gradient descent and learned primal-dual.

Quantifying Model Uncertainty in Inverse Problems via Bayesian Deep Gradient Descent


Neural style transfer aims to create artistic images by synthesizing patterns from a given style image. Recently, the Adaptive Instance Normalization (AdaIN) layer is proposed to achieve real-time arbitrary style transfer. However, we observed that if crucial features based on AdaIN can be further emphasized during transfer, both content and style information will be better reflected in stylized images. Furthermore, it is always essential to preserve more details and reduce unexpected artifacts in order to generate appealing results. In this paper, we introduce an improved arbitrary style transfer method based on the self-attention mechanism. A self-attention module is designed to learn what and where to emphasize in the input image. In addition, an extra Laplacian loss is applied to preserve structure details of the content while eliminating artifacts. Experimental results demonstrate that the proposed method outperforms AdaIN and can generate more appealing results.

Arbitrary Style Transfer with Parallel Self-Attention

we designed a high-efficient detector that combines ThunderNet and Region Growing Detector(RGD) to detect road signs, and built a fusion model of long short term memory network (LSTM) and KF in the state estimation and the color histogram. The experimental results demonstrate that the proposed method improved the state estimation accuracy by 6.4% and enhanced the Frames Per Second(FPS) to 41.

An Fast Road Sign Tracking algorithm

The scene graph of an image is an explicit, concise representation of the image; hence, it can be used in various applications such as visual question answering or robot vision. We propose a novel neural network model for generating scene graphs that maintain global consistency, which prevents the generation of unrealistic scene graphs; the performance in the scene graph generation task is expected to improve. Our proposed model is used to construct a hierarchical structure whose leaf nodes correspond to objects depicted in the image, and a message is passed along the estimated structure on the fly. To this end, we aggregate features of all objects into the root node of the hierarchical structure, and the global context is back-propagated to the root node to maintain all the object nodes. The experimental results on the Visual Genome dataset indicate that the proposed model outperformed the existing models in scene graph generation tasks. We further qualitatively confirmed that the hierarchical structures captured by the proposed model seemed to be valid.

Exploring and Exploiting the Hierarchical Structure of a Scene for Scene Graph Generation

Person re-identification (Re-ID) has attracted a lot of research attention in recent years. However, supervised methods demand enormous amount of manually annotated data. In this paper, we propose a Self-Paced bottom-up Clustering Network with Side Information (SPCNet-SI) for unsupervised person Re- ID, where the side information comes from the serial number of the camera associated with each image. Specifically, our proposed SPCNet-SI exploits the camera side information to guide the feature learning and uses soft label in bottom-up clustering process, in which the camera association information is used in the repelled loss and the soft label based cluster information is used to select the candidate cluster pairs to merge. Moreover, a self-paced dynamic mechanism is developed to regularize the merging process such that the clustering is implemented in an easy-to-hard way with a slow-to-fast merging process. Experiments on two benchmark datasets Market-1501 and DukeMTMC-ReID demonstrate promising performance.

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