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检索条件"任意字段=International Conference on Signal and Data Processing, ICSDP 2019"

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FREE LUNCH FOR CROSS-DOMAIN OCCLUDED FACE RECOGNITION WITHOUT SOURCE data 47

FREE LUNCH FOR CROSS-DOMAIN OCCLUDED FACE RECOGNITION WITHOU...

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47th IEEE international conference on Acoustics, Speech and signal processing (ICASSP)

作者： Zhang, Taoshan Xiang, Youjun Li, Xianfeng Weng, Zichun Chen, Zhen Fu, Yuli South China Univ Technol Sch Elect & Informat Engn Guangzhou 510641 Peoples R China

ISBN: (纸本)9781665405409

Most recognizing occluded faces methods focus on synthetic-occluded faces for training due to the lack of real-occluded data. However, the performance may suffer from degradation since the synthetic-occluded and real-occluded face images are under different distributions. Hence, it draws our eyes to transfer the model from the synthetic to the real-world domain. In this paper, we propose a source data-free domain adaptive occluded face recognition framework to optimize the network in the target domain via redefining it as a pseudo labels denoising problem. To obtain reliable pseudo labels, we train synthetic-occluded and non-occluded images via distribution alignment to extract occlusion-robust features. Nonetheless, completely correct labels are still unattainable. Then, a denoising strategy is proposed to optimize pseudo labels by centroid-based feature clustering. Experiments show that the proposed approach can effectively recognize the real-occluded face;it also reminds the occluded faces recognition community about the feasibility of domain adaptation in existing tasks.

关键词： occluded face recognition adversarial learning domain adaptation source data-free

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Boosting Face Recognition Performance with Synthetic data and Limited Real data 48

Boosting Face Recognition Performance with Synthetic Data an...

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48th IEEE international conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Wang, Wenqing Zhang, Lingqing Pun, Chi-Man Xie, Jiu-Cheng University of Macau China Nanjing University of Posts and Telecommunications China

ISBN: (纸本)9781728163277

Face recognition is one of the most precise and straightforward methods to establish individual identity, and is important in our daily life. To solve the issues of privacy, bias, and collection difficulty caused by face recognition relying heavily on collecting a huge number of real face images from the Internet, a seemingly promising idea is to employ GAN-generated synthetic faces as the training data. However, there are obvious surface gaps and domain gaps between real and synthetic face images, and cannot be replaced directly. In this paper, we attempt to boost face recognition simultaneously using synthetic data and limited real data. Specifically, we first design an augmented space for auto augmentation methods to augment synthetic images to alleviate the surface gap, then propose to disentangle the underlying style distributions through dual batch normalization layers so that both synthetic and real images can be learned jointly by convolution layers without mixing across domains. Extensive experiments demonstrate our method can achieve better results than training with large quantities of real data. © 2023 IEEE.

关键词： Face recognition

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ENHANCED U-TRANSFORMER NETWORKS FOR AUTOMATIC PULMONARY VESSEL SEGMENTATION IN CT IMAGES 30

ENHANCED U-TRANSFORMER NETWORKS FOR AUTOMATIC PULMONARY VESS...

引用

30th IEEE international conference on Image processing (ICIP)

作者： Qi, Hao Jin, Jiabao Ding, Gang Chen, Xiangxing Ke, Sunkui Chen, Yinran Luo, Xiongbiao Xiamen Univ Dept Comp Sci & Technol Xiamen 361005 Peoples R China Xiamen Univ Natl Inst Data Sci Hlth & Med Xiamen 361102 Peoples R China Xiamen Univ Zhongshan Hosp Xiamen 361004 Peoples R China

ISBN: (纸本)9781728198354

Pulmonary vessel CT segmentation is important to clinical diagnosis of lung diseases. But it is still a challenge due to limited CT quality and complicated vascular structures. This paper proposes new enhanced U-transformer networks that combine transformers, a contrast enhancement block with a reverse attention block to perform end-to-end vessel segmentation. Specifically, the contrast enhancement block directly augments edge or structural information while the reverse attention block conducts the network paying more attention to blurred boundaries and uncertain regions of vessels, leading to improving the accuracy and smoothness of pulmonary vessel segmentation. We validated our proposed method on 50 CT volumes selected from LIDC-IDRI, with the experimental results demonstrating that it works more effectively and stably than currently available approaches. Particularly, the average dice similarity coefficient and recall were improved from (85.23%, 85.37%) to (86.07%, 86.67%), respectively.

关键词： Pulmonary Vessel Segmentation Reverse Attention Block Contrast Enhancement Block

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MIXED IN TIME AND MODALITY: CURSE OR BLESSING? CROSS-INSTANCE data AUGMENTATION FOR WEAKLY SUPERVISED MULTIMODAL TEMPORAL FUSION 47

MIXED IN TIME AND MODALITY: CURSE OR BLESSING? CROSS-INSTANC...

引用

47th IEEE international conference on Acoustics, Speech and signal processing (ICASSP)

作者： Zhu, Yonggang Tian, Chao Jiang, Zhuqing Men, Aidong Wang, Haiying Chen, Qingchao Beijing Univ Posts & Commun Beijing Peoples R China China Xian Satellite Control Ctr Xian Peoples R China Peking Univ Beijing Peoples R China

ISBN: (纸本)9781665405409

In multimodal video event localization, we usually leverage feature fusion across different axes, such as the modality and temporal axes, for better context. To reduce the costs of detailed annotations, recent solutions explore weakly supervised settings. However, we observe that when feature fusion meets weakly supervised localization, problems can occur. It may cause "feature cross-interference", which produces a smearing effect on the localization result and can't be effectively supervised with conventional multiple instance learning loss. We verify it quantitatively on the audio-visual video parsing (AVVP) task, and propose a cross-instance data-augmentation framework, which can preserve the benefits of feature fusion while providing explicit feedbacks for feature cross-interference. We show that our method can enhance performance of existing models on two weakly supervised audio-visual localization tasks, i.e. AVVP and AVE.

关键词： feature cross-interference data augmentation weakly supervised audio-visual localization

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Unsupervised Pre-Training for data-Efficient Text-to-Speech on Low Resource Languages 48

Unsupervised Pre-Training for Data-Efficient Text-to-Speech ...

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48th IEEE international conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Park, Seongyeon Song, Myungseo Kim, Bohyung Oh, Tae-Hyun Cnai Seoul Korea Republic of Postech Dept. of Ee and Gsai Pohang Korea Republic of Yonsei University Institute for Convergence Research and Education in Advanced Technology Seoul Korea Republic of

ISBN: (纸本)9781728163277

Neural text-to-speech (TTS) models can synthesize natural human speech when trained on large amounts of transcribed speech. How-ever, collecting such large-scale transcribed data is expensive. This paper proposes an unsupervised pre-training method for a sequence-to-sequence TTS model by leveraging large untranscribed speech data. With our pre-training, we can remarkably reduce the amount of paired transcribed data required to train the model for the target downstream TTS task. The main idea is to pre-train the model to reconstruct de-warped mel-spectrograms from warped ones, which may allow the model to learn proper temporal assignment relation between input and output sequences. In addition, we propose a data augmentation method that further improves the data efficiency in finetuning. We empirically demonstrate the effectiveness of our proposed method in low-resource language scenarios, achieving outstanding performance compared to competing methods. The code and audio samples are available at: https://***/cnaigithub/SpeechDewarping © 2023 IEEE.

关键词： data augmentation data-efficiency pre-training Text-to-speech unsupervised learning

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SELF-KNOWLEDGE DISTILLATION BASED SELF-SUPERVISED LEARNING FOR COVID-19 DETECTION FROM CHEST X-RAY IMAGES 47

SELF-KNOWLEDGE DISTILLATION BASED SELF-SUPERVISED LEARNING F...

引用

47th IEEE international conference on Acoustics, Speech and signal processing (ICASSP)

作者： Li, Guang Togo, Ren Ogawa, Takahiro Haseyama, Miki Hokkaido Univ Grad Sch Informat Sci & Technol Sapporo Hokkaido Japan Hokkaido Univ Educ & Res Ctr Math & Data Sci Sapporo Hokkaido Japan Hokkaido Univ Fac Informat Sci & Technol Sapporo Hokkaido Japan

ISBN: (纸本)9781665405409

The global outbreak of the Coronavirus 2019 (COVID-19) has overloaded worldwide healthcare systems. Computer-aided diagnosis for COVID-19 fast detection and patient triage is becoming critical. This paper proposes a novel self-knowledge distillation based self-supervised learning method for COVID-19 detection from chest X-ray images. Our method can use self-knowledge of images based on similarities of their visual features for self-supervised learning. Experimental results show that our method achieved an HM score of 0.988, an AUC of 0.999, and an accuracy of 0.957 on the largest open COVID-19 chest X-ray dataset.

关键词： COVID-19 chest X-ray images self-knowledge distillation self-supervised learning

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An Explainable Raman Spectral Classification Pipeline via NMF and SHAP: A Case Study of Pen Ink Colors

An Explainable Raman Spectral Classification Pipeline via NM...

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2024 Asia Pacific signal and Information processing Association Annual Summit and conference, APSIPA ASC 2024

作者： Lapsatid, Pongpon Deepaisarn, Somrudee Eiamchai, Pitak Sirindhorn International Institute of Technology Thammasat University Pathum Thani Thailand Research Unit in Sustainable Electrochemical Intelligent Thammasat University Pathum Thani Thailand National Electronics and Computer Technology Center Pathum Thani Thailand

ISBN: (纸本)9798350367331

Ink analysis, especially the analysis of coloring components, plays a crucial role in forensic science, as it provides useful information to validate the authenticity of the questioned documents. Raman spectroscopy offers a nondestructive and time- and cost-effective alternative to forensic investigations, providing unique spectral fingerprints of pen ink samples. There are several attempts to analyze Raman spectra using computational techniques. This work proposes an explainable pipeline for Raman spectral classification to distinguish pen ink colors. Preprocessing steps were applied to improve the spectral signal-to-noise ratio. data augmentation was conducted to increase the training data. Using the weights of the 48 extracted non-negative matrix factorization (NMF) components as inputs for the Random Forest classifier, the test accuracy of 0.8456 was achieved. Moreover, NMF allows underlying linear compositions of spectra associated with ink color classes to be observed. SHapley Additive exPlanations (SHAP) then enables a quantification of the average impact of each NMF component on the classification. Through this pipeline, the scalar multiplication between NMF components, their weights, and their SHAP values enables a human-intuitive explainability of the model prediction results that captures unique Raman spectral patterns as expected for each ink color class. © 2019 Institute of Physics Publishing. All rights reserved.

关键词： Non-negative matrix factorization

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HYBRID MODEL-BASED / data-DRIVEN GRAPH TRANSFORM FOR IMAGE CODING 29

HYBRID MODEL-BASED / DATA-DRIVEN GRAPH TRANSFORM FOR IMAGE C...

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IEEE international conference on Image processing (ICIP)

作者： Bagheri, Saghar Do, Tam Thuc Cheung, Gene Ortega, Antonio York Univ Toronto ON Canada Univ Southern Calif Los Angeles CA USA

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

Transform coding to sparsify signal representations remains crucial in an image compression pipeline. While the Karhunen-Loeve transform (KLT) computed from an empirical covariance matrix C is theoretically optimal for a stationary process, in practice, collecting sufficient statistics from a non-stationary image to reliably estimate C can be difficult. In this paper, to encode an intra-prediction residual block, we pursue a hybrid model-based / data-driven approach: the first K eigenvectors of a transform matrix are derived from a statistical model, e.g., the asymmetric discrete sine transform (ADST), for stability, while the remaining N - K are computed from C for data adaptivity. The transform computation is posed as a graph learning problem, where we seek a graph Laplacian matrix minimizing a graphical lasso objective inside a convex cone sharing the first K eigenvectors in a Hilbert space of real symmetric matrices. We efficiently solve the problem via augmented Lagrangian relaxation and proximal gradient (PG). Using open-source WebP as a baseline image codec, experimental results show that our hybrid graph transform achieved better coding performance than discrete cosine transform (DCT), ADST and KLT, and better stability than KLT.

关键词： Image coding graph transform graph learning

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Realization of RF signal over Bidirectional Optical Wireless Communication Links 59

Realization of RF Signal over Bidirectional Optical Wireless...

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59th Annual IEEE international conference on Communications (IEEE ICC)

作者： Zhang, Runxin Zhang, Yuheng Li, Menghan Liu, Qikai Lu, Lu Chinese Acad Sci Technol & Engn Ctr Space Utilizat Key Lab Space Utilizat Beijing 100094 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

ISBN: (纸本)9798350304060;9798350304053

Different from radio frequency (RF) signals which may use time division duplexing (TDD), frequency division duplexing (FDD) or other duplex techniques, visible light communications (VLC) have faced a critical issue in transmitting RF based wireless signals directly without changing any existing signal processing designs. As defined by the IEEE 802.11bb standard, transmitting and receiving the originally bidirectional duplex-mode RF signals from one antenna, e.g., the time division multiple access (TDMA)-like wireless fidelity (WiFi) signals, using two unidirectional VLC links is challenging. In VLC the light-emitting diodes (LED) and PIN diodes cannot work in a duplex mode. To address the mismatch problem between VLC and RF, while carrying RF signals on top of the VLC links, we propose a light-based RF transceiver design, named LiRF, capable of seamlessly converting RF signals into VLC. The proposed LiRF architecture, comprising a frequency detection module, a bidirectional signals separation module, and an optical transceiver module, is feasible for any RF communication system. To validate the real impact of LiRF, we build a prototype using separate components and two IEEE 802.11ax (WiFi-6) network interface cards (NICs) in the 5GHz channel. Compared with state-of-the art offline single-directional WiFi-over-VLC system (e.g., WoV), LiRF are designed for bidirectional data transmissions with real-time applications support capability. Experimental results indicate that LiRF links can support 720 Mbps data streams using a LED-PIN transceiver. LiRF is a transparent VLC system design supporting WiFi NICs, and it is proved to be able to bridge the gap between VLC and RF communications, which paves the way for the smooth integration of VLC into existing and future RF systems.

关键词： Visible Light Communications WiFi LiFi Wireless Communication Networks.

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FULLY AUTOMATIC CERVICAL VERTEBRAE SEGMENTATION VIA ENHANCED U²-NET 30

FULLY AUTOMATIC CERVICAL VERTEBRAE SEGMENTATION VIA ENHANCED...

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30th IEEE international conference on Image processing (ICIP)

作者： Zhang, Fan Zheng, Linya Chen, Yinran Lin, Chen Huang, Liping Bai, Yuming Luo, Xiongbiao Xiamen Univ Dept Comp Sci & Technol Xiamen 361005 Peoples R China Xiamen Univ Natl Inst Data Sci Hlth & Med Xiamen 361102 Peoples R China Xiamen Med Coll Stomatol Hosp Xiamen 361016 Peoples R China

ISBN: (纸本)9781728198354

Accurate segmentation of the cervical vertebrae in CT images can assist clinicians in analyzing the adolescent patient's growth and development and establishing an effective orthodontic plan. This work develops an enhanced U2-Net architecture for fully automatic cervical vertebrae segmentation in CT images. Specifically, such an enhanced architecture first creates a deepwise separable residual U-shape module (DUM) in different levels and a convolutional attention module embedded into a U-structure for encoding and decoding and obtains a coarse segmentation. Then, it reuses a DUM to refine the segmentation. We evaluated our method on 60 CT scans, with the experimental results showing that our method attains much better segmentation performance than state-of-the-art network models. Particularly, it can improve the dices similarity coefficient, intersection over union, precision, and recall from (0.9586, 0.9207, 0.9584, 0.9591) to (0.9755, 0.9524, 0.9832, 0.9708), respectively, while it can reduce the model parameters from 44.0M to 16.5M.

关键词： Medical Image Segmentation Cervical Vertebrae Segmentation Deep Learning U2-Net

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