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FewSOME: One-Class Few Shot Anomaly Detection with Siamese Networks

FewSOME: One-Class Few Shot Anomaly Detection with Siamese N...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Niamh Belton Misgina Tsighe Hagos Aonghus Lawlor Kathleen M. Curran Science Foundation Ireland Centre for Research Training in Machine Learning School of Medicine University College Dublin School of Computer Science University College Dublin Insight Centre for Data Analytics University College Dublin Dublin Ireland

Recent Anomaly Detection techniques have progressed the field considerably but at the cost of increasingly complex training pipelines. Such techniques require large amounts of training data, resulting in computationally expensive algorithms that are unsuitable for settings where only a small amount of normal samples are available for training. We propose ‘Few Shot anOMaly dEtection’ (FewSOME), a deep One-Class Anomaly Detection algorithm with the ability to accurately detect anomalies having trained on ‘few’ examples of the normal class and no examples of the anomalous class. We describe FewSOME to be of low complexity given its low data requirement and short training time. FewSOME is aided by pretrained weights with an architecture based on Siamese Networks. By means of an ablation study, we demonstrate how our proposed loss, ‘Stop Loss’, improves the robustness of FewSOME. Our experiments demonstrate that FewSOME performs at stateof-the-art level on benchmark datasets MNIST, CIFAR-10, F-MNIST and MVTec AD while training on only 30 normal samples, a minute fraction of the data that existing methods are trained on. Moreover, our experiments show FewSOME to be robust to contaminated datasets. We also report F1 score and balanced accuracy in addition to AUC as a benchmark for future techniques to be compared against. Code available; https://***/niamhbelton/FewSOME.

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A Novel Robust Color Tag for a Highly Efficient Soccer Robot vision System 22

A Novel Robust Color Tag for a Highly Efficient Soccer Robot...

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22nd ieee/ACIS International conference on computer and Information Science, ICIS 2022

作者： Sun, Yan Sun, Shaorong Shanghai University School of Computer Engineering and Science Shanghai China University of Shanghai for Science and Technology Business School Shanghai China

ISBN: (数字)9781665494632

ISBN: (纸本)9781665494632

Soccer robot league is a competition on the cooperation between multi-robots in a highly dynamic environment. Getting accurate and precise information on time in a computationally constrained environment is challenging. In the small size league, each robot has a unique colored tag on the top for identification. Traditional color tag maximizes the colored pattern to give a stable recognition result, however, it might cause recognition confusion in the competitive game. This paper proposes a novel rotation-invariant tag to avoid the confusion without extra recognition constrains. A highly efficient vision system is constructed, each module is optimized to improve the recognition efficiency and robustness. The system focuses on the small size league but capable to be transferred to other leagues. The experiments evaluate the time consumption of each part and the whole system. The results demonstrate that recognizing the robot through our novel identity tag by the highly optimized system has achieved the real-time standard. © 2022 ieee.

关键词： Color

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TCCT-Net: Two-Stream Network Architecture for Fast and Efficient Engagement Estimation via Behavioral Feature Signals

TCCT-Net: Two-Stream Network Architecture for Fast and Effic...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Alexander Vedernikov Puneet Kumar Haoyu Chen Tapio Seppänen Xiaobai Li Center for Machine Vision and Signal Analysis University of Oulu Finland State Key Laboratory of Blockchain and Data Security Zhejiang University China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Engagement analysis finds various applications in healthcare, education, advertisement, services. Deep Neural Networks, used for analysis, possess complex architecture and need large amounts of input data, computational power, inference time. These constraints challenge embedding systems into devices for real-time use. To address these limitations, we present a novel two-stream feature fusion "Tensor-Convolution and Convolution-Transformer Network" (TCCT-Net) architecture. To better learn the meaningful patterns in the temporal-spatial domain, we design a "CT" stream that integrates a hybrid convolutional-transformer. In parallel, to efficiently extract rich patterns from the temporal-frequency domain and boost processing speed, we introduce a "TC" stream that uses Continuous Wavelet Transform (CWT) to represent information in a 2D tensor form. Evaluated on the EngageNet dataset, the proposed method outperforms existing baselines, utilizing only two behavioral features (head pose rotations) compared to the 98 used in baseline models. Furthermore, comparative analysis shows TCCT-Net’s architecture offers an order-of-magnitude improvement in inference speed compared to state-of-the-art image-based Recurrent Neural Network (RNN) methods. The code will be released at https://***/vedernikovphoto/TCCT_Net.

关键词： Convolutional codes Accuracy Recurrent neural networks Continuous wavelet transforms Wavelet domain Convolution Computational modeling

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HMANet: Hybrid Multi-Axis Aggregation Network for Image Super-Resolution

HMANet: Hybrid Multi-Axis Aggregation Network for Image Supe...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Shu-Chuan Chu Zhi-Chao Dou Jeng-Shyang Pan Shaowei Weng Junbao Li College of Computer Science and Engineering Shandong University of Science and Technology School of Artificial Intelligence Nanjing University of Information Science and Technology School of Information Engineering Guangdong University of Technology School of Electronic and Information Engineering Harbin Institute of Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Transformer-based methods have demonstrated excellent performance on super-resolution visual tasks, surpassing conventional convolutional neural networks. However, existing work typically restricts self-attention computation to non-overlapping windows to save computational costs. This means that Transformer-based networks can only use input information from a limited spatial range. Therefore, a novel Hybrid Multi-Axis Aggregation network (HMA) is proposed in this paper to exploit feature potential information better. HMA is constructed by stacking Residual Hybrid Transformer Blocks(RHTB) and Grid Attention Blocks(GAB). On the one side, RHTB combines channel attention and self-attention to enhance nonlocal feature fusion and produce more attractive visual results. Conversely, GAB is used in cross-domain information interaction to jointly model similar features and obtain a larger perceptual field. For the super-resolution task in the training phase, a novel pre-training method is designed to enhance the model representation capabilities further and validate the proposed model’s effectiveness through many experiments. The experimental results show that HMA outperforms the state-of-the-art methods on the benchmark dataset. We provide code and models at https://***/korouuuuu/HMA.

关键词： Training Visualization Convolution Superresolution Stacking Benchmark testing Transformers

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Do More With What You Have: Transferring Depth-Scale from Labeled to Unlabeled Domains

Do More With What You Have: Transferring Depth-Scale from La...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Alexandra Dana Nadav Carmel Amit Shomer Ofer Manela Tomer Peleg Samsung Israel R&D Center Tel Aviv Israel

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Transferring the absolute depth prediction capabilities of an estimator to a new domain is a task with significant real-world applications. This task is specifically challenging when images from the new domain are collected without ground-truth depth measurements, and possibly with sensors of different intrinsics. To overcome such limitations, a recent zero-shot solution was trained on an extensive training dataset and encoded the various camera intrinsics. Other solutions generated synthetic data with depth labels that matched the intrinsics of the new target data to enable depth-scale transfer between the *** this work we present an alternative solution that can utilize any existing synthetic or real dataset, that has a small number of images annotated with ground truth depth labels. Specifically, we show that self-supervised depth estimators result in up-to-scale predictions that are linearly correlated to their absolute depth values across the domain, a property that we model in this work using a single scalar. In addition, aligning the field-of-view of two datasets prior to training, results in a common linear relationship for both domains. We use this observed property to transfer the depth-scale from source datasets that have absolute depth labels to new target datasets that lack these measurements, enabling absolute depth predictions in the target *** suggested method was successfully demonstrated on the KITTI, DDAD and nuScenes datasets, while using other existing real or synthetic source datasets, that have a different field-of-view, other image style or structural content, achieving comparable or better accuracy than other existing methods that do not use target ground-truth depths.

关键词： Training Image sensors computer vision Accuracy conferences Predictive models Cameras

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Multi-Objective Hardware Aware Neural Architecture Search using Hardware Cost Diversity

Multi-Objective Hardware Aware Neural Architecture Search us...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Nilotpal Sinha Peyman Rostami Abd El Rahman Shabayek Anis Kacem Djamila Aouada SnT University of Luxembourg

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Hardware-aware Neural Architecture Search approaches (HW-NAS) automate the design of deep learning architectures, tailored specifically to a given target hardware platform. Yet, these techniques demand substantial computational resources, primarily due to the expensive process of assessing the performance of identified architectures. To alleviate this problem, a recent direction in the literature has employed representation similarity metric for efficiently evaluating architecture performance. Nonetheless, since it is inherently a single objective method, it requires multiple runs to identify the optimal architecture set satisfying the diverse hardware cost constraints, thereby increasing the search cost. Furthermore, simply converting the single objective into a multi-objective approach results in an under-explored architectural search space. In this study, we propose a Multi-Objective method to address the HW-NAS problem, called MO-HDNAS, to identify the trade-off set of architectures in a single run with low computational cost. This is achieved by optimizing three objectives: maximizing the representation similarity metric, minimizing hardware cost, and maximizing the hardware cost diversity. The third objective, i.e. hardware cost diversity, is used to facilitate a better exploration of the architecture search space. Experimental results demonstrate the effectiveness of our proposed method in efficiently addressing the HW-NAS problem across six edge devices for the image classification task.

关键词： Performance evaluation Costs Image edge detection computer architecture Search problems Hardware Computational efficiency

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Speech2UnifiedExpressions: Synchronous Synthesis of Co-Speech Affective Face and Body Expressions from Affordable Inputs

Speech2UnifiedExpressions: Synchronous Synthesis of Co-Speec...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Uttaran Bhattacharya Aniket Bera Dinesh Manocha Adobe Inc. San Jose CA USA Purdue University West Lafayette IN USA University of Maryland College Park MD USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We present a multimodal learning-based method to simultaneously synthesize co-speech facial expressions and upper-body gestures for digital characters using RGB video data captured using commodity cameras. Our approach learns from sparse face landmarks and upper-body joints, estimated directly from video data, to generate plausible emotive character motions. Given a speech audio waveform and a token sequence of the speaker’s face landmark motion and body-joint motion computed from a video, our method synthesizes the motion sequences for the speaker’s face landmarks and body joints to match the content and the affect of the speech. We design a generator consisting of a set of encoders to transform all the inputs into a multimodal embedding space capturing their correlations, followed by a pair of decoders to synthesize the desired face and pose motions. To enhance the plausibility of synthesis, we use an adversarial discriminator that learns to differentiate between the face and pose motions computed from the original videos and our synthesized motions based on their affective expressions. To evaluate our approach, we extend the TED Gesture Dataset to include view-normalized, co-speech face landmarks in addition to body gestures. We demonstrate the performance of our method through thorough quantitative and qualitative experiments on multiple evaluation metrics and via a user study. We observe that our method results in low reconstruction error and produces synthesized samples with diverse facial expressions and body gestures for digital characters. The relevant source code and dataset are available at https://***/UttaranB127/speech2unified_expressions.

关键词： Measurement Learning systems computer vision Correlation Face recognition Source coding conferences

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ReweightOOD: Loss Reweighting for Distance-based OOD Detection

ReweightOOD: Loss Reweighting for Distance-based OOD Detecti...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Sudarshan Regmi Bibek Panthi Yifei Ming Prashnna K Gyawali Danail Stoyanov Binod Bhattarai University College London UK NepAl Applied Mathematics and Informatics Institute for Research Nepal University of Wisconsin-Madison USA West Virginia University USA University of Aberdeen UK

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Out-of-Distribution (OOD) detection is crucial for ensuring safety and reliability of neural networks in critical applications. Distance-based OOD detection is based on the assumption that OOD samples are mapped far from In-Distribution (ID) clusters in embedding space. A recent approach for obtaining OOD-detection-friendly embedding space has been contrastive optimization of pulling similar pairs and pushing apart dissimilar pairs. It assigns equal significance to all similarity instances with the implicit objective of maximizing the mean proximity between samples with their corresponding hypothetical class centroids. However, the emphasis should be directed towards reducing the Minimum Enclosing Sphere (MES) for each class and achieving higher inter-class dispersion to effectively mitigate the potential for ID-OOD overlap. Optimizing low-signal dissimilar pairs might potentially act against achieving maximal inter-class dispersion while less-optimized similar pairs prevent achieving smaller MES. Based on this, we propose a reweighting scheme ReweightOOD, that adopts the similarity optimization which prioritizes the optimization of less-optimized contrasting pairs while assigning lower importance to already well-optimized contrasting pairs. Such a reweighting scheme serves to minimize the MES for each class while achieving maximal interclass dispersion. Experimental results on a challenging CIFAR100 benchmark using ResNet-18 network demonstrate that ReweightOOD outperforms supervised contrastive loss by a whopping 38% in the average FPR metric. In various classification datasets, our method provides a promising solution for enhancing OOD detection capabilities in neural networks.

关键词： Measurement computer vision conferences Neural networks Benchmark testing Safety pattern recognition

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DiCo-NeRF: Difference of Cosine Similarity for Neural Rendering of Fisheye Driving Scenes

DiCo-NeRF: Difference of Cosine Similarity for Neural Render...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Jiho Choi Gyutae Hwang Sang Jun Lee Jeonbuk National University Republic of Korea

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Neural radiance fields have emerged in the field of autonomous driving, which contributes to improve perception of the complex 3D environment through the reconstruction of geometry and appearance. Moving objects and sky for outdoor environment is challenging to optimize the NeRF model. Previous work addresses these challenges through preprocessing such as masking; however, the masking process requires additional ground-truth data and a segmentation network. We propose DiCo-NeRF, an approach for driving scenes by leveraging cosine similarity map differences of vision-language aligned model. DiCo-NeRF investigates the correlation between rendered patches and predefined text and adjusts the loss of challenging patches, such as moving objects and the sky. Our neural radiance field utilizes embedding vectors from a pre-trained CLIP to obtain the cosine similarity maps. We introduce SimLoss, a loss function aimed at regulating the color field of NeRF based on the quantified distribution differences between ground-truth and rendered similarity maps. Unlike previous NeRF models that used driving datasets, our approach does not require additional input, such as sensor data, to the model. Experimental results demonstrate that the incorporation of language semantic cues improves the performance of the novel view synthesis task, particularly in complex driving environments. We conducted experiments that included fisheye driving scenes from the KITTI-360 and real-world datasets. Our code is available at https://***/ziiho08/DiCoNeRF.

关键词： Three-dimensional displays Image color analysis Computational modeling Semantics Neural radiance field Rendering (computer graphics) Vectors

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Tri-VAE: Triplet Variational Autoencoder for Unsupervised Anomaly Detection in Brain Tumor MRI

Tri-VAE: Triplet Variational Autoencoder for Unsupervised An...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Hansen Wijanarko Evelyne Calista Li-Fen Chen Yong-Sheng Chen National Yang Ming Chiao Tung University Taiwan

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The intricate manifestations of pathological brain lesions in imaging data pose challenges for supervised detection methods due to the scarcity of annotated samples. To overcome this difficulty, our focus shifts to unsupervised anomaly detection. In this work, we exclusively train the proposed model using healthy data to identify unseen anomalies during testing. This study entails investigating the triplet-based variational autoencoder to simultaneously learn the distribution of healthy brain data and denoising capabilities. Importantly, we rectify a misconception inherent in prior projection-based approaches which relies on the presumption that healthy regions within images would persist unaltered in the reconstructed output. This inadvertently implied a substantial likeness in latent space representations between lesion and lesion-free images. However, this assumption might not hold true, particularly due to the potential significant impact of lesion area intensities on the projection process notably for autoencoders with single information bottleneck. To overcome this limitation, we disentangled metric learning from latent sampling. This approach ensures that both lesion and lesion-free input images are projected into the same distribution, specifically the lesion-free projection. Moreover, we introduce a semantic-guided gated cross skip module to enhance spatial detail retrieval while suppressing anomalies, leveraging robust healthy brain representation semantics exist in the deeper levels of the decoder. We also discovered that incorporating structure similarity index measure as an extra training objective bolsters the capability of anomaly detection for the proposed model.

关键词： Training Measurement Pathology Semantics Noise reduction Brain modeling pattern recognition

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