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Investigate indistinguishable points in semantic segmentation of 3D point cloud

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arXiv 2021年

作者： Xu, Mingye Zhou, Zhipeng Zhang, Junhao Qiao, Yu ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences University of Chinese Academy of Sciences China Shanghai AI Lab Shanghai China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society

This paper investigates the indistinguishable points (difficult to predict label) in semantic segmentation for large-scale 3D point clouds. The indistinguishable points consist of those located in complex boundary, points with similar local textures but different categories, and points in isolate small hard areas, which largely harm the performance of 3D semantic segmentation. To address this challenge, we propose a novel Indistinguishable Area Focalization Network (IAF-Net), which selects indistinguishable points adaptively by utilizing the hierarchical semantic features and enhance fine-grained features for points especially those indistinguishable points. We also introduce multi-stage loss to improve the feature representation in a progressive way. Moreover, in order to analyze the segmentation performances of indistinguishable areas, we propose a new evaluation metric called Indistinguishable Points Based Metric (IPBM). Our IAF-Net achieves the comparable results with state-of-the-art performance on several popular 3D point cloud datasets e.g. S3DIS and ScanNet, and clearly outperforms other methods on IPBM. Our code will be available at https://***/MingyeXu/IAF-Net. Copyright © 2021, The Authors. All rights reserved.

关键词： Semantic Segmentation

Combined Trojan Y Chromosome strategy and sterile insect technique to eliminate mosquitoes: Modelling and analysis

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arXiv 2021年

作者： Lyu, Jingjing Gu, Musong Wang, Sheng College Of Computer Science Chengdu University Chengdu China Key Laboratory of Pattern Recognition and Intelligent Information Processing Institutions of Higher Education of Sichuan Province Chengdu University Chengdu China Information Development and Management Center Chuzhou University Chuzhou China

Sterile insect technique has been successfully applied in the control of agricultural pests, however, it has a limited ability to control mosquitoes. A promising alternative approach is Trojan Y Chromosome strategy, which works by manipulating the sex ratio of a population through the introduction of feminized Y Y supermales that guarantee male offspring. A combined Trojan Y chromosome strategy and sterile insect technique (TYC-SIT) strategy is modeled with ordinary differential equations that allow the kinetics of the female population decline of mosquitoes to be evaluated under identical modeling conditions. The dynamical analysis leads to results on both local and global stabilities of this combined model. Optimal control analysis is also implemented to investigate the optimal mechanisms for extinction of mosquitoes. In particular, the numerical results affirm that the combined TYC-SIT enables near elimination of mosquitoes. The conclusion has great significance for pest controls. © 2021, CC BY.

关键词： Ordinary differential equations

UMFA: A photorealistic style transfer method based on U-Net and multi-layer feature aggregation

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arXiv 2021年

作者： Rao, Dongyu Wu, Xiao-Jun Li, Hui Kittler, Josef Xu, Tianyang Jiangnan University Jiangsu Provincial Engineerinig Laboratory of Pattern Recognition and Computational Intelligence School of Artificial Intelligence and Computer Science Lihu Avenue Wuxi214122 China University of Surrey Centre for Vision Speech and Signal Processing GuildfordGU2 7XH United Kingdom

In this paper, we propose a photorealistic style transfer network to emphasize the natural effect of photo realistic image stylization. In general, distortion of the image content and lacking of details are two typical issues in the style transfer field. To this end, we design a novel framework employing the U-Net structure to maintain the rich spatial clues, with a multi-layer feature aggregation (MFA) method to simultaneously provide the details obtained by the shallow layers in the stylization processing. In particular, an encoder based on the dense block and a decoder form a symmetrical structure of U-Net are jointly staked to realize an effective feature extraction and image reconstruc tion. Besides, a transfer module based on MFA and "adaptive instance normalization" (AdaIN) is inserted in the skip connection positions to achieve the stylization. Accordingly, the stylized image possesses the texture of a real photo and preserves rich content details without introducing any mask or post-processing steps. The experimental results on public datasets demonstrate that our method achieves a more faithful structural similarity with a lower style loss, reflecting the effectiveness and merit of our approach. © 2021, CC0.

关键词： Deep learning

Natural Image Matting Based on Surrogate Model

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SSRN

SSRN 2022年

作者： Liang, Yihui Gou, Hongshan Feng, Fujian Liu, Guisong Huang, Han School of Computer Science Zhongshan Institute University of Electronic Science and Technology of China Zhongshan528400 China Guizhou Key Laboratory of Pattern Recognition and Intelligent System Guizhou Minzu University Guiyang550025 China School of Economic Information Engineering Southwestern University of Finance and Economics Chengdu610000 China Laboratory of Intelligent Algorithms and Intelligent Software School of Software Engineering South China University of Technology Guangzhou510006 China

Image matting is important in digital image processing. The pixel pair optimization-based methods are some of the image matting methods that have distinct advantages in spatially disconnected foreground. However, they cannot provide high-quality alpha mattes with limited computing resources which limits their computing resource-sensitive applications. To address this problem, we present a natural image matting method based on a surrogate model. The surrogate model for pixel pair optimization is established in the presented method to approximate pixel pair fitness, and its analytical optimal solution is used as the approximate optimal solution of pixel pair optimization problem, which saves a lot of computing resources. Experimental results show that the image matting method based on a surrogate model provides high-quality matting mattes with low computing resources and its image matting performance is competitive against state-of-the-art pixel pair optimization-based methods which require a lot of computational resources. © 2022, The Authors. All rights reserved.

关键词： Pixels

Towards Phytoplankton Parasite Detection Using Autoencoders

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arXiv 2023年

作者： Bilik, Simon Batrakhanov, Daniel Eerola, Tuomas Haraguchi, Lumi Kraft, Kaisa Van den Wyngaert, Silke Kangas, Jonna Sjöqvist, Conny Madsen, Karin Lensu, Lasse Kälviäinen, Heikki Horak, Karel Computer Vision and Pattern Recognition Laboratory Department of Computational Engineering Lappeenranta-Lahti University of Technology LUT Yliopistonkatu 34 Lappeenranta53850 Finland Department of Control and Instrumentation Faculty of Electrical Engineering and Communication Brno University of Technology Technická 3058/10 Brno61600 Czech Republic Marine Ecology Measurements Finnish Environment Institute Agnes Sjöbergin Katu 2 Helsinki00790 Finland Department of Biology University of Turku Vesilinnantie 5 Turku20014 Finland Environmental and Marine Biology Åbo Akademi University Henrikinkatu 2 Turku20014 Finland

Phytoplankton parasites are largely understudied microbial components with a potentially significant ecological impact on phytoplankton bloom dynamics. To better understand their impact, we need improved detection methods to integrate phytoplankton parasite interactions in monitoring aquatic ecosystems. Automated imaging devices usually produce high amount of phytoplankton image data, while the occurrence of anomalous phytoplankton data is rare. Thus, we propose an unsupervised anomaly detection system based on the similarity of the original and autoencoder-reconstructed samples. With this approach, we were able to reach an overall F1 score of 0.75 in nine phytoplankton species, which could be further improved by species-specific fine-tuning. The proposed unsupervised approach was further compared with the supervised Faster R-CNN based object detector. With this supervised approach and the model trained on plankton species and anomalies, we were able to reach the highest F1 score of 0.86. However, the unsupervised approach is expected to be more universal as it can detect also unknown anomalies and it does not require any annotated anomalous data that may not be always available in sufficient quantities. Although other studies have dealt with plankton anomaly detection in terms of non-plankton particles, or air bubble detection, our paper is according to our best knowledge the first one which focuses on automated anomaly detection considering putative phytoplankton parasites or infections. © 2023, CC BY.

关键词： Object detection

Multiple domain experts collaborative learning: Multi-source domain generalization for person re-identification

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arXiv 2021年

作者： Yu, Shijie Zhu, Feng Chen, Dapeng Zhao, Rui Chen, Haobin Zhu, Jinguo Tang, Shixiang Qiao, Yu ShenZhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences University of Chinese Academy of Sciences China SenseTime Group Limited Shanghai AI Lab Shanghai China Qing Yuan Research Institute Shanghai Jiao Tong University Shanghai China

Recent years have witnessed significant progress in person re-identification (ReID). However, current ReID approaches still suffer from considerable performance degradation when unseen testing domains exhibit different characteristics from the source training ones, known as the domain generalization problem. Given multiple source training domains, previous Domain Generalizable ReID (DG-ReID) methods usually learn all domains together using a shared network, which can't learn sufficient knowledge from each domain. In this paper, we propose a novel Multiple Domain Experts Collaborative Learning (MECL) framework for better exploiting all training domains, which benefits from the proposed Domain-Domain Collaborative Learning (DDCL) and Universal-Domain Collaborative Learning (UDCL). DDCL utilizes domain-specific experts for fully exploiting each domain, and prevents experts from over-fitting the corresponding domain using a meta-learning strategy. In UDCL, a universal expert supervises the learning of domain experts and continuously gathers knowledge from all domain experts. Note, only the universal expert will be used for inference. Extensive experiments on DG-ReID benchmarks demonstrate the effectiveness of DDCL and UDCL, and show that the whole MECL framework significantly outperforms state-of-the-arts. Experimental results on DG-classification benchmarks also reveal the great potential of applying MECL to other DG tasks. Code will be released. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

Let's Play Music: Audio-Driven Performance Video Generation

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Let's Play Music: Audio-Driven Performance Video Generation

International Conference on pattern recognition

作者： Hao Zhu Yi Li Feixia Zhu Aihua Zheng Ran He Center for Research on Intelligent Perception and Computing (CRIPAC) National Laboratory of Pattern Recognition (NLPR) CASIA Beijing China Center for Excellence in Brain Science and Intelligence Technology CAS Beijing China Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University

We propose a new task named Audio-driven Performance Video Generation (APVG), which aims to synthesize the video of a person playing a certain instrument guided by a given music audio clip. It is a challenging task to generate the high-dimensional temporal consistent videos from low-dimensional audio modality. In this paper, we propose a multi-staged framework to generate realistic and synchronized performance video from given music. Firstly, we provide both global appearance and local spatial information by generating the coarse videos and keypoints of body and hands from a given music respectively. Then, we propose to transform the generated keypoints to heatmap via a differentiable space transformer, since the heatmap provides more spatial information but is harder to generate directly from audio. Finally, we propose a Structured Temporal UNet (STU) to extract both intra-frame structured information and interframe temporal consistency. They are obtained via graph-based structure module, and CNN-GRU based high-level temporal module respectively for final video generation. Comprehensive experiments validate the effectiveness of our proposed framework.

关键词： Instruments Transforms Synchronization Data mining Task analysis Space heating

Feature refinement: An expression-specific feature learning and fusion method for micro-expression recognition

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arXiv 2021年

作者： Zhou, Ling Mao, Qirong Huang, Xiaohua Zhang, Feifei Zhang, Zhihong School of Computer Science and Communication Engineering Jiangsu University ZhenjiangJiangsu212013 China School of Computer Engineering Nanjing Institute of Technology China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China Xiamen University Xiamen China Center for Machine Vision and Signal Analysis University of Oulu Finland

Micro-Expression recognition has become challenging, as it is extremely difficult to extract the subtle facial changes of micro-expressions. Recently, several approaches proposed several expression-shared features algorithms for micro-expression recognition. However, they do not reveal the specific discriminative characteristics, which lead to sub-optimal performance. This paper proposes a novel Feature Refinement (FR) with expression-specific feature learning and fusion for micro-expression recognition. It aims to obtain salient and discriminative features for specific expressions and also predict expression by fusing the expression-specific features. FR consists of an expression proposal module with attention mechanism and a classification branch. First, an inception module is designed based on optical flow to obtain expression-shared features. Second, in order to extract salient and discriminative features for specific expression, expression-shared features are fed into an expression proposal module with attention factors and proposal loss. Last, in the classification branch, labels of categories are predicted by a fusion of the expression-specific features. Experiments on three publicly available databases validate the effectiveness of FR under different protocol. Results on public benchmarks demonstrate that our FR provides salient and discriminative information for micro-expression recognition. The results also show our FR achieves better or competitive performance with the existing state-of-the-art methods on micro-expression recognition. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep learning

Local Gradient Difference Features for Classification of 2D-3D Natural Scene Text Images

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Local Gradient Difference Features for Classification of 2D-...

International Conference on pattern recognition

作者： Lokesh Nandanwar Palaiahnakote Shivakumara Ramachandra Raghavendra Tong Lu Umapada Pal Daniel Lopresti Nor Badrul Anuar Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Faculty of Information Technology and Electrical Engineering IIK NTNU Norway National Key Lab for Novel Software Technology Nanjing University Nanjing China Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Computer Science & Engineering Lehigh University Bethlehem PA USA

Methods developed for normal 2D text detection do not work well for text that is rendered using decorative, 3D effects, etc. This paper proposes a new method for classification of 2D and 3D natural scene text images so that an appropriate recognition method can be chosen accordingly based on the classification results for better performance. The proposed method explores local gradient differences for obtaining candidate pixels, which represent a stroke. To study the spatial distribution of candidate pixels, we propose a measure, called COLD, which is denser for pixels toward the center of strokes and scattered for non-stroke pixels. This observation leads us to introduce mass features for extracting the regular spatial pattern of COLD, which indicates a 2D text image. The extracted features are fed into a Neural Network (NN) for classification. The proposed method is tested on (i) a new dataset introduced in this work (ii) a second dataset assembled from standard natural scene datasets (iii) Non-Text Image datasets which does not contain text, rather it contains objects. Experimental results of the proposed method on images with text and non-text show that the proposed method is independent of text. The proposed approach improves text detection and recognition performance significantly after classification.

关键词： Three-dimensional displays Image recognition Graphical models Text recognition Artificial neural networks Feature extraction Standards