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Age-Group Classification of Facial Images

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Age-Group Classification of Facial Images

International Conference on Machine Learning and Applications (ICMLA)

作者： Li Liu Jianming Liu Jun Cheng Shenzhen Institutes of Advanced Technolozv Chinese Academv of Science Computer Science and Engineering Department School of Guilin University of Electronic Technology Guilin China Guangdong Provincial Key Laboratory of Robotics and Intelligent System

This paper presents the age-group classification based on facial images. We perform age-group classification by dividing ages into five age groups according to the incremental regulation of age. Features are extracted from face images through Active Appearance Model (AAM), which describe the shape and gray value variation of face images. Principle Component Analysis (PCA) is adopted to reduce the dimensions and Support Vector Machine (SVM) classifier with Gaussian Radian Basis Function (RBF) kernel is trained. Experimental results demonstrate that AAM can improve the performance of age estimation.

关键词： Machine learning

Augmented Box Replay: Overcoming Foreground Shift for Incremental Object Detection

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Augmented Box Replay: Overcoming Foreground Shift for Increm...

International Conference on computer vision (ICCV)

作者： Yuyang Liu Yang Cong Dipam Goswami Xialei Liu Joost van de Weijer State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences University of Chinese Academy of Sciences South China University of Technology Computer Vision Center Barcelona VCIP CS Nankai University Department of Computer Science Universitat Autònoma de Barcelona

In incremental learning, replaying stored samples from previous tasks together with current task samples is one of the most efficient approaches to address catastrophic forgetting. However, unlike incremental classification, image replay has not been successfully applied to incremental object detection (IOD). In this paper, we identify the overlooked problem of foreground shift as the main reason for this. Foreground shift only occurs when replaying images of previous tasks and refers to the fact that their background might contain foreground objects of the current task. To overcome this problem, a novel and efficient Augmented Box Replay (ABR) method is developed that only stores and replays foreground objects and thereby circumvents the foreground shift problem. In addition, we propose an innovative Attentive RoI Distillation loss that uses spatial attention from region-of-interest (RoI) features to constrain current model to focus on the most important information from old model. ABR significantly reduces forgetting of previous classes while maintaining high plasticity in current classes. Moreover, it considerably reduces the storage requirements when compared to standard image replay. Comprehensive experiments on Pascal-VOC and COCO datasets support the state-of-the-art performance of our model 1 .

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MM-3DScene: 3D Scene Understanding by Customizing Masked Modeling with Informative-Preserved Reconstruction and Self-Distilled Consistency

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arXiv 2022年

作者： Xu, Mingye Xu, Mutian He, Tong Ouyang, Wanli Wang, Yali Han, Xiaoguang Qiao, Yu The Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China SSE CUHKSZ China University of Chinese Academy of Sciences China Shanghai Artificial Intelligence Laboratory China FNii CUHKSZ China

Masked Modeling (MM) has demonstrated widespread success in various vision challenges, by reconstructing masked visual patches. Yet, applying MM for large-scale 3D scenes remains an open problem due to the data sparsity and scene complexity. The conventional random masking paradigm used in 2D images often causes a high risk of ambiguity when recovering the masked region of 3D scenes. To this end, we propose a novel informative-preserved reconstruction, which explores local statistics to discover and preserve the representative structured points, effectively enhancing the pretext masking task for 3D scene understanding. Integrated with a progressive reconstruction manner, our method can concentrate on modeling regional geometry and enjoy less ambiguity for masked reconstruction. Besides, such scenes with progressive masking ratios can also serve to self-distill their intrinsic spatial consistency, requiring to learn the consistent representations from unmasked areas. By elegantly combining informative-preserved reconstruction on masked areas and consistency self-distillation from unmasked areas, a unified framework called MM-3DScene is yielded. We conduct comprehensive experiments on a host of downstream tasks. The consistent improvement (e.g., +6.1% mAP@0.5 on object detection and +2.2% mIoU on semantic segmentation) demonstrates the superiority of our approach. © 2022, CC BY-NC-SA.

关键词： Distillation

Pseudo-label Guided Cross-video Pixel Contrast for Robotic Surgical Scene Segmentation with Limited Annotations

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arXiv 2022年

作者： Yu, Yang Zhao, Zixu Jin, Yueming Chen, Guangyong Dou, Qi Heng, Pheng-Ann The Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China The Department of Computer Science University College London United Kingdom Zhejiang Lab China

Surgical scene segmentation is fundamentally crucial for prompting cognitive assistance in robotic surgery. However, pixel-wise annotating surgical video in a frame-by-frame manner is expensive and time consuming. To greatly reduce the labeling burden, in this work, we study semi-supervised scene segmentation from robotic surgical video, which is practically essential yet rarely explored before. We consider a clinically suitable annotation situation under the equidistant sampling. We then propose PGV-CL, a novel pseudo-label guided cross-video contrast learning method to boost scene segmentation. It effectively leverages unlabeled data for a trusty and global model regularization that produces more discriminative feature representation. Concretely, for trusty representation learning, we propose to incorporate pseudo labels to instruct the pair selection, obtaining more reliable representation pairs for pixel contrast. Moreover, we expand the representation learning space from previous image-level to cross-video, which can capture the global semantics to benefit the learning process. We extensively evaluate our method on a public robotic surgery dataset EndoVis18 and a public cataract dataset CaDIS. Experimental results demonstrate the effectiveness of our method, consistently outperforming the state-of-the-art semi-supervised methods under different labeling ratios, and even surpassing fully supervised training on EndoVis18 with 10.1% labeling. Our code is available at https://***/yangyu-cuhk/PGVCL. Copyright © 2022, The Authors. All rights reserved.

关键词： Robotic surgery

BTI Aging Monitoring based on SRAM Start-up Behavior

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BTI Aging Monitoring based on SRAM Start-up Behavior

Asian Test Symposium (ATS)

作者： Shengyu Duan Peng Wang Gaole Sai School of Computer Engineering and Science Shanghai University Shanghai China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology China

ISBN: (数字)9781728174679

ISBN: (纸本)9781728174686

Bias Temperature Instability (BTI) is one of the dominant CMOS aging mechanisms. It causes time-dependent variation, threatening circuit lifetime reliability. BTI-induced circuit errors are not detectable at the fabrication stage. On-line monitoring schemes are therefore necessary to capture the degradations during the operational time. Traditional aging monitoring techniques exhibit high implementation complexity and low stability. In this paper, we propose a BTI monitoring approach by simply tracking the start-up behavior of SRAM cells. SRAM is a widely used on-chip device in many applications. We study the impact of BTI for SRAM start-up values and age some cells in a manipulated manner. The BTI degradation is evaluated based on the number of SRAM cells starting with a certain value. This technique can be used to estimate the degradation for on-chip logic circuits without introducing additional circuitry, and thus has very low implementation complexity. We use an SRAM array with 1024 cells to estimate the degradations for multiple logic circuits, and show the average mean absolute percentage error as 8.48%. In addition, this technique is robust considering process, voltage and temperature variations.

关键词： Degradation Monitoring Logic circuits Transistors SRAM cells Aging System-on-chip

Few Shot Transfer Active Learning for Logo Detection in Sports Video

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SSRN

SSRN 2022年

作者： Su, Hang Qiu, Guoping College of Electronic and Information Engineering Shenzhen University Shenzhen518052 China Guangdong Key Lab for Intelligent Information Processing Shenzhen University Shenzhen518052 China Shenzhen Institute of AI and Robotics for Society Shenzhen518172 China Pengcheng Laboratory Shenzhen518055 China School of Computer Science University of Nottingham NottinghamNG8 1BB United Kingdom

We exploit the power of deep convolutional neural network (DCNN) and take advantage of established datasets from existing applications to develop a deep transfer active learning (DTAL) algorithm to select the most valuable samples such that we can label the smallest number of samples to achieve the maximum performance improvements in training video object detection models. By exploiting possible shared common deep feature space between static dataset and video dataset through transfer learning based on highly adaptable DCNN features, DTAL implements a diversity based active learning to select the most informative samples from a sequence of similar image frames for video object detection. We have successfully applied the new DTAL algorithm to implement active learning for logo detection from live streaming sports videos as well as pedestrian and face detection from video data. We show that DTAL is a better active learning method outperforming state of the art deep learning based active learning techniques. In addition, we contribute one of the largest video based logo datasets, sports match video logo (SMVL), to facilitate research in logo detection in general and in the applications of transfer learning and active learning to exploit static object detection datasets for video object detection in particular. © 2022, The Authors. All rights reserved.

关键词： Object recognition

Clustering-Based Representation Learning through Output Translation and Its Application to Remote-Sensing Images

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arXiv 2021年

作者： Li, Qinglin Li, Bin Garibaldi, Jonathan M. Qiu, Guoping College of Electronic and Information Engineering Shenzhen University Shenzhen518052 China Guangdong Key Lab for Intelligent Information Processing Shenzhen University Shenzhen518052 China School of Computer Science The University of Nottingham NottinghamNG8 1BB United Kingdom Shenzhen Institute of AI and Robotics for Society Shenzhen518172 China Pengcheng Laboratory Shenzhen518055 China

In supervised deep learning, learning good representations for remote-sensing images (RSI) relies on manual annotations. However, in the area of remote sensing, it is hard to obtain huge amounts of labeled data. Recently, self-supervised learning shows its outstanding capability to learn representations of images, especially the methods of instance discrimination. Comparing methods of instance discrimination, clustering-based methods not only view the transformations of the same image as "positive" samples but also similar images. In this paper, we propose a new clustering-based method for representation learning. We first introduce a quantity to measure representations' discriminativeness and from which we show that even distribution requires the most discriminative representations. This provides a theoretical insight into why evenly distributing the images works well. We notice that only the even distributions that preserve representations' neighborhood relations are desirable. Therefore, we develop an algorithm that translates the outputs of a neural network to achieve the goal of evenly distributing the samples while preserving outputs' neighborhood relations. Extensive experiments have demonstrated that our method can learn representations that are as good as or better than the state of the art approaches, and that our method performs computationally efficiently and robustly on various RSI datasets. The paper has been published on Remote Sensing https://***/10.3390/rs14143361. Copyright © 2021, The Authors. All rights reserved.

关键词： Remote sensing

FeCAM: Exploiting the Heterogeneity of Class Distributions in Exemplar-Free Continual Learning

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arXiv 2023年

作者： Goswami, Dipam Liu, Yuyang Twardowski, Bartlomiej van de Weijer, Joost Department of Computer Science Universitat Autònoma de Barcelona Spain Computer Vision Center Barcelona Spain University of Chinese Academy of Sciences China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences China IDEAS-NCBR

Exemplar-free class-incremental learning (CIL) poses several challenges since it prohibits the rehearsal of data from previous tasks and thus suffers from catastrophic forgetting. Recent approaches to incrementally learning the classifier by freezing the feature extractor after the first task have gained much attention. In this paper, we explore prototypical networks for CIL, which generate new class prototypes using the frozen feature extractor and classify the features based on the Euclidean distance to the prototypes. In an analysis of the feature distributions of classes, we show that classification based on Euclidean metrics is successful for jointly trained features. However, when learning from non-stationary data, we observe that the Euclidean metric is suboptimal and that feature distributions are heterogeneous. To address this challenge, we revisit the anisotropic Mahalanobis distance for CIL. In addition, we empirically show that modeling the feature covariance relations is better than previous attempts at sampling features from normal distributions and training a linear classifier. Unlike existing methods, our approach generalizes to both many- and few-shot CIL settings, as well as to domain-incremental settings. Interestingly, without updating the backbone network, our method obtains state-of-the-art results on several standard continual learning benchmarks. Code is available at https://***/dipamgoswami/FeCAM. © 2023, CC BY.

关键词： Normal distribution

Spatial Pyramid Pooling based Convolutional Autoencoder Network for Loop Closure Detection

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Spatial Pyramid Pooling based Convolutional Autoencoder Netw...

IEEE International Conference on Real-time Computing and robotics (RCAR)

作者： Rong Xiang Yuan Liu Qieshi Zhang Jun Cheng School of Computer Science and Information Security Guilin University of Electronic Technology GuangXi China Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China The Chinese University of Hong Kong Hong Kong China

ISBN: (数字)9781728137261

ISBN: (纸本)9781728137278

Loop closure detection is a crucial module in simultaneous localization and mapping (SLAM), which reduces accumulative error in building environment map. Traditional appearance-based methods for loop closure detection are vulnerable to environmental variations as they mainly rely on hand-crafted features. The convolutional neural networks (ConvNets) can automatically learn feature representation from original image, and it is more robust to illumination changes. However, the ConvNets methods may fail when the viewpoint changes significantly due to it extract global features. In order to solve the problem mentioned above, in this paper, we design an unsupervised network which combines the advantage of the traditional and ConvNets methods, and propose a new module named spatial pyramid pooling based convolution autoencoder (SPP-CAE). We evaluate the performance of the proposed method on several open datasets using precision-recall metric. The results show that our method is feasible for detecting loops and is more robust than state-of-the-art methods.

关键词： Feature extraction Convolution Lighting Visualization Training Simultaneous localization and mapping Network architecture