Italy

This paper focuses on two fundamental graph recognition tasks: node classification and graph classification. Existing methods usually learn the node and graph representations for these two tasks separately, and ignore modeling the relations between the local and global structures. In this paper, we propose a general approach to learn the local and global features collaboratively: (1) in order to characterize the correlation among nodes and communities (a set of nodes), we employ the joint community detection/dynamic routing modules to generate the clustering assignment matrices at first and then utilize these matrices to cluster nodes to capture the global information of graphs (locally relevant graph representations). Inspired by the success of spectral clustering, we minimize the ratiocut loss to help optimize the learned assignment matrices. (2) We maximize the mutual information between local and global representations to help learn the globally relevant node representations. Experimental results on a variety of node and graph classification benchmarks show that our model can achieve superior performance over the state-of-the-art approaches.

ICPR 2020

A General Model for Learning Node and Graph Representations Jointly

hierarchical information learning

graph classification

representation learning

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

Recently, language representation techniques have achieved great performances in text classification. However, most existing representation models are specifically designed for English materials, which may fail in Chinese because of the huge difference between these two languages. Actually, few existing methods for Chinese text classification process texts at a single level. However, as a special kind of hieroglyphics, radicals of Chi- nese characters are good semantic carriers. In addition, Pinyin codes carry the semantic of tones, and Wubi reflects the stroke structure information, etc. Unfortunately, previous researches neglected to find an effective way to distill the useful parts of these four factors and to fuse them. In our works, we propose a novel model called Moto: Enhancing Embedding with Multiple Joint Factors. Specifically, we design an attention mechanism to distill the useful parts by fusing the four-level information above more effectively. We conduct extensive experiments on four popular tasks. The empirical results show that our Moto achieves SOTA 0.8316 (F1-score, 2.11% improvement) on Chinese news titles, 96.38 (1.24% improvement) on Fudan Corpus and 0.9633 (3.26% improvement) on THUCNews.

Moto: Enhancing Embedding with Multiple Joint Factors for Chinese Text Classification

Recently, neural language representation models pre-trained on large corpus can capture rich co-occurrence information and be fine-tuned in downstream tasks to improve the performance. As a result, they have achieved state-of-the-art results in a large range of language tasks. However, there exists other valuable semantic information such as similar, opposite, or other possible meanings in external knowledge graphs (KGs). We argue that entities in KGs could be used to enhance the correct semantic meaning of language sentences. In this paper, we propose a new method CKG: Dynamic Representation Based on Context and Knowledge Graph. On the one side, CKG can extract rich semantic information from a large corpus. On the other side, it can make full use of inside information such as co-occurrence in large corpus and outside information such as similar entities in KGs. We conduct extensive experiments on a wide range of tasks, including QQP, MRPC, SST-5, SQuAD, CoNLL 2003, and SNLI. The experiment results show that CKG achieves SOTA 89.2 on SQuAD compared with SAN (84.4), ELMo (85.8), and BERT_Base (88.5).

CKG: Dynamic Representation Based on Context and Knowledge Graph

In this paper we address motion segmentation, that is the problem of clustering points in multiple images according to a number of moving objects. Two-frame correspondences are assumed as input without prior knowledge about trajectories. Our method is based on principles from “multi-model fitting” and “permutation synchronization”, and – differently from previous techniques working under the same assumptions – it can handle an unknown number of motions. The proposed approach is validated on standard datasets, showing that it can correctly estimate the number of motions while maintaining comparable or better accuracy than the state of the art.

Motion segmentation with pairwise matches and unknown number of motions


We'll present the challenges for breast tumor detection and the proposed approach, including the flowchart, experimental results, and contributions.

Breast Anatomy Enriched Tumor Saliency Estimation

Various anomaly detection methods utilizing different types of images have recently been proposed. However, anomaly detection in the field of planetary science is still done predominantly by the human eye because explainability is crucial in the physical sciences and most of today’s anomaly detection methods based on deep learning cannot offer enough. Moreover, preparing a large number of images required for fully utilizing anomaly detection is not always feasible. In this work, we propose a new framework that automatically detects large bow-shaped structures (stationary waves) appearing on the surface of the Venus clouds by applying a variational auto-encoder (VAE) and attention maps to anomaly detection. We also discuss the advantages of using image augmentation. Experiments show that our approach can achieve higher accuracy than the state-of-theart methods even when the anomaly images are scarce. On the basis of this finding, we discuss anomaly detection frameworks particularly suited to physical science domains.

Automatic Detection of Stationary Waves in the Venus Atmosphere Using Deep Generative Models


As with many imaging tasks, disparity estimation for light fields seems to be well-matched to machine learning approaches. Neural network-based methods can achieve an overall bad pixel rate as low as four percent on the 4D light field benchmark dataset, continued effort to improve accuracy is resulting in diminishing returns. On the other hand, due to the growing importance of mobile and embedded devices, improving efficiency is emerging as an important problem. In this paper, we improve the efficiency of existing neural net approaches for light field disparity estimation by introducing efficient network blocks, pruning redundant sections of the network, and downsampling the resolution of the feature vector. To improve performance, we also propose densely sampled epipolar image plane volumes as input. Experiment results show that our approach can achieve similar results compared with state-of-the-art methods while using only one-tenth runtime.

Fast and Efficient Network for Field Disparity Estimation

Anomalous human activity detection is the task of identifying human activities that differ from the usual. Existing techniques, in general, try to deploy some samples from an open-set (anomalous activities can not be represented as a closed set) to define the discriminator. However, it is non-trivial to obtain novel activity instances. To this end, we propose PoseCVAE, a novel anomalous human activity detection strategy using the notion of generative modeling. We adopt a hybrid training strategy comprising of self-supervised and unsupervised learning. The self-supervised learning helps the encoder and decoder to learn better latent space representation of human pose trajectories. We train our framework to predict future pose trajectory given a normal track of past poses, i.e., the goal is to learn a conditional posterior distribution that represents normal training data. To achieve this we use a novel adaptation of a conditional variational autoencoder (CVAE) and refer it as PoseCVAE. Future pose prediction will be erroneous if the given poses are sampled from a distribution different from the learnt posterior, which is indeed the case with abnormal activities. To further separate the abnormal class, we imitate abnormal poses in the encoded space by sampling from a distinct mixture of gaussians (MoG). We use a binary cross-entropy (BCE) loss as a novel addition to the standard CVAE loss function to achieve this. We test our framework on three publicly available datasets and achieve comparable performance to existing unsupervised methods that exploit pose information.

PoseCVAE: Anomalous Human Activity Detection

Dense depth maps play an important role in Computer Vision and AR (Augmented Reality). For CV applications, a dense depth map is the cornerstone of 3D reconstruction allowing real objects to be precisely displayed in the computer. And Dense depth maps can handle correct occlusion relationships between virtual content and real objects for better user experience in AR. However, the complicated computation limits the development of computing dense depth maps. We present a novel algorithm that produces low latency, spatio-temporally smooth dense depth maps using only a CPU. The depth maps exhibit sharp discontinuities at depth edges in low computational complexity ways. Our algorithm obtains the sparse SLAM reconstruction first, then extracts coarse depth edges from a down-sampled RGB image by morphology operations. Next, we thin the depth edges and align them with image edges. Finally, an effective initialization scheme and an improved optimization solver are adopted to accelerate convergence. We evaluate our proposal quantitatively and the result shows improvements on the accuracy of depth map with respect to other state-of-the-art and baseline techniques.

Edge-Aware Monocular Dense Depth Estimation with Morphology


Recently, many researches propose to employ attention based encoder-decoder models to convert a sequence of trajectory points into a LaTeX string for online handwritten mathematical expression recognition (OHMER), and the recognition performance of these models critically relies on the accuracy of the attention. In this paper, unlike previous methods which basically employ a soft attention model, we propose to employ a posterior attention model, which modifies the attention probabilities after observing the output probabilities generated by the soft attention model. In order to further improve the posterior attention mechanism, we propose a stroke average pooling layer to aggregate point-level features obtained from the encoder into stroke-level features. We argue that posterior attention is better to be implemented on stroke-level features than point-level features as the output probabilities generated by stroke is more convincing than generated by point, and we prove that through experimental analysis. Validated on the CROHME competition task, we demonstrate that stroke based posterior attention achieves expression recognition rates of 54.26% on CROHME 2014 and 51.75% on CROHME 2016. According to attention visualization analysis, we empirically demonstrate that the posterior attention mechanism can achieve better alignment accuracy than the soft attention mechanism.

Stroke Based Posterior Attention for Online Handwritten Mathematical Expression Recognition

Spoken Language Understanding(SLU) is an essential part of the spoken dialogue system, which typically consists of intent detection (ID) and slot filling (SF) tasks. Recently, recurrent neural networks(RNNs) based methods achieved state-of-the-art for SLU. It is noted that, in the existing RNN-based approaches, ID and SF tasks are often jointly modeled to utilize the correlation information between them. However, we noted that, so far, the efforts to obtain better performance by supporting bidirectional and explicit information exchange between ID and SF are not well studied. In addition, few studies attempt to capture the local context information to enhance the performance of SF. Motivated by these findings, in this paper, Parallel Interactive Network(PIN) is proposed to model the mutual guidance between ID and SF. Specifically, given an utterance, a Gaussian self-attentive encoder is introduced to generate the context-aware feature embedding of the utterance which is able to capture local context information. Taking the feature embedding of the utterance, the Slot2Intent module and Intent2Slot module are developed to capture the bidirectional information flow for IDand SF tasks. Finally, a cooperation mechanism is constructed to fuse the information obtained from Slot2Intent and Intent2Slot modules to further reduce the prediction bias. The experiments on two benchmark datasets,i.e., SNIPS and ATIS, demonstrate the effectiveness of our approach, which achieves a competitive result with state-of-the-art models. More encouragingly, by using the feature embedding of the utterance generated by the pre-trained language model BERT, our method achieves the state-of-the-art among all comparison approaches.

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A General Model for Learning Node and Graph Representations Jointly

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