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Strain Detection based on Breath and Motion Features Obtained by a Force Sensor for Smart Toilet Systems

Strain Detection based on Breath and Motion Features Obtaine...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Akaho, Rina Yoshioka, Mototaka Panasonic Ind Corp Osaka Japan

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Aging people may be prone to accidents in bathrooms and toilets. The detection of strain motion for a smart toilet application has not been studied sufficiently. In this paper, we propose a method for strain detection from a force sensor placed on a toilet seat for a smart toilet healthcare application. The method first extracts breath and motion features that are assumed to be key components for the strain detection. The method then learns the discriminator model based on the random forest classifier using the aforementioned features. Finally, the method recognizes actions in the toilet room. There were five detection actions: seating, taking up toilet paper, wiping bottom, which are normal actions when sitting on a toilet seat, and strain actions (strong and weak). An experiment with 19 subjects was also conducted. Compared with a microwave sensor-based recognition, which is a conventional method (accuracy = 61.6%), our method was able to recognize the actions with high accuracy of 80.2% (significant test: T = 12.7, P < 0.01) in the experiment. Our strain detection method has the potential to be used as a smart toilet system to prevent blood pressure elevation and collapse caused by strain in the future.

关键词： conferences Windings Time series analysis Medical services Feature extraction Microwave theory and techniques pattern recognition

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ViTOL: vision Transformer for Weakly Supervised Object Localization

ViTOL: Vision Transformer for Weakly Supervised Object Local...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Gupta, Saurav Lakhotia, Sourav Rawat, Abhay Tallamraju, Rahul Mercedes Benz Res & Dev India Bengaluru India

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Weakly supervised object localization (WSOL) aims at predicting object locations in an image using only image-level category labels. Common challenges that image classification models encounter when localizing objects are, (a) they tend to look at the most discriminative features in an image that confines the localization map to a very small region, (b) the localization maps are class agnostic, and the models highlight objects of multiple classes in the same image and, (c) the localization performance is affected by background noise. To alleviate the above challenges we introduce the following simple changes through our proposed method ViTOL. We leverage the vision-based transformer for self-attention and introduce a patch-based attention dropout layer (p-ADL) to increase the coverage of the localization map and a gradient attention rollout mechanism to generate class-dependent attention maps. We conduct extensive quantitative, qualitative and ablation experiments on the ImageNet-1K and CUB datasets. We achieve state-of-the-art MaxBoxAcc-V2 localization scores of 70.47% and 73.17% on the two datasets respectively.

关键词： Location awareness Measurement Visualization Shape Semantics computer architecture Transformers

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Is synthetic voice detection research going into the right direction?

Is synthetic voice detection research going into the right d...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Borzi, Stefano Giudice, Oliver Stanco, Filippo Allegra, Dario Univ Catania Dept Math & Comp Sci Catania Italy Banca Italia IT Dept Appl Res Team Rome Italy

ISBN: (纸本)9781665487399

Machine Learning, and in general Artificial Intelligence approaches, brought a great advance in each and every field of computer Science increasing accuracy levels of predictors in any known problem. Indeed, this evolution enabled the construction of effective frameworks and solutions able to be used in investigative and forensics scenarios for detection of fakes and, in general, manipulations in multimedia contents. On the other hand, can we trust these systems? Is research activity going in the right direction? Are we just taking the low-hanging fruit without taking into account many real-case-in-the-wild situations? The purpose of this paper is to raise an alert to the research community in the specific context of synthetic voice detection, where data available for training is not big enough to give sufficient trust in the techniques available in the literature. To this aim, an exploratory investigation of the most common voice spoofing dataset was carried out and it was surprisingly easy to build simple classifiers without any Deep Learning techniques. Simple considerations on bitrate were sufficient to achieve an effective detection performance.

关键词： Deep learning Training computer vision Image color analysis Forensics conferences Bit rate

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Weakly-Supervised Action Detection Guided by Audio Narration

Weakly-Supervised Action Detection Guided by Audio Narration

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Ye, Keren Kovashka, Adriana Cruise San Francisco CA 94107 USA Univ Pittsburgh Pittsburgh PA USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Videos are more well-organized curated data sources for visual concept learning than images. Unlike the 2-dimensional images which only involve the spatial information, the additional temporal dimension bridges and synchronizes multiple modalities. However, in most video detection benchmarks, these additional modalities are not fully utilized. For example, EPIC Kitchens is the largest dataset in first-person (egocentric) vision, yet it still relies on crowdsourced information to refine the action boundaries to provide instance-level action annotations. We explored how to eliminate the expensive annotations in video detection data which provide refined boundaries. We propose a model to learn from the narration supervision and utilize multimodal features, including RGB, motion flow, and ambient sound. Our model learns to attend to the frames related to the narration label while suppressing the irrelevant frames from being used. Our experiments show that noisy audio narration suffices to learn a good action detection model, thus reducing annotation expenses.

关键词： Visualization Annotations Soft sensors Refining Detectors pattern recognition Synchronization

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ResNeSt: Split-Attention Networks

ResNeSt: Split-Attention Networks

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Zhang, Hang Wu, Chongruo Zhang, Zhongyue Zhu, Yi Lin, Haibin Zhang, Zhi Sun, Yue He, Tong Mueller, Jonas Manmatha, R. Li, Mu Smola, Alexander Meta Menlo Pk CA 94025 USA Univ Calif Davis Davis CA USA Snap Santa Monica CA USA Amazon Seattle WA USA ByteDance Beijing Peoples R China SenseTime Hong Kong Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The ability to learn richer network representations generally boosts the performance of deep learning models. To improve representation-learning in convolutional neural networks, we present a multi-branch architecture, which applies channel-wise attention across different network branches to leverage the complementary strengths of both feature-map attention and multi-path representation. Our proposed Split-Attention module provides a simple and modular computation block that can serve as a drop-in replacement for the popular residual block, while producing more diverse representations via cross-feature interactions. Adding a Split-Attention module into the architecture design space of RegNet-Y and FBNetV2 directly improves the performance of the resulting network. Replacing residual blocks with our Split-Attention module, we further design a new variant of the ResNet model, named ResNeSt, which outperforms EfficientNet in terms of the accuracy/latency trade-off.

关键词： Deep learning computer vision Computational modeling conferences computer architecture pattern recognition Convolutional neural networks

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Less is More: Proxy Datasets in NAS approaches

Less is More: Proxy Datasets in NAS approaches

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Moser, Brian Raue, Federico Hees, Joern Dengel, Andreas German Res Ctr Artificial Intelligence DFKI Darmstadt Germany TU Kaiserslautern Kaiserslautern Germany

ISBN: (纸本)9781665487399

Neural Architecture Search (NAS) defines the design of Neural Networks as a search problem. Unfortunately, NAS is computationally intensive because of various possibilities depending on the number of elements in the design and the possible connections between them. In this work, we extensively analyze the role of the dataset size based on several sampling approaches for reducing the dataset size (unsupervised and supervised cases) as an agnostic approach to reduce search time. We compared these techniques with four common NAS approaches in NAS-Bench-201 in roughly 1,400 experiments on CIFAR-100. One of our surprising findings is that in most cases we can reduce the amount of training data to 25%, consequently also reducing search time to 25%, while at the same time maintaining the same accuracy as if training on the full dataset. In addition, some designs derived from subsets out-perform designs derived from the full dataset by up to 22 p.p. accuracy.

关键词： Training computer vision conferences Neural networks Training data computer architecture Search problems

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Fourier Image Transformer

Fourier Image Transformer

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Buchholz, Tim-Oliver Jug, Florian FMI Biomed Res Basel Switzerland Fdn Human Technopole Milan Italy

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Transformer architectures show spectacular performance on NLP tasks and have recently also been used for tasks such as image completion or image classification. Here we propose to use a sequential image representation, where each prefix of the complete sequence describes the whole image at reduced resolution. Using such Fourier Domain Encodings (FDEs), an auto-regressive image completion task is equivalent to predicting a higher resolution output given a low-resolution input. Additionally, we show that an encoder-decoder setup can be used to query arbitrary Fourier coefficients given a set of Fourier domain observations. We demonstrate the practicality of this approach in the context of computed tomography (CT) image reconstruction. In summary, we show that Fourier Image Transformer (FIT) can be used to solve relevant image analysis tasks in Fourier space, a domain inherently inaccessible to convolutional architectures.

关键词： Image resolution Image coding Computed tomography computer architecture Image representation Transformers pattern recognition

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On Improving Cross-dataset Generalization of Deepfake Detectors

On Improving Cross-dataset Generalization of Deepfake Detect...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Nadimpalli, Aakash Varma Rattani, Ajita Wichita State Univ Sch Comp Wichita KS 67260 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Facial manipulation by deep fake has caused major security risks and raised severe societal concerns. As a countermeasure, a number of deep fake detection methods have been proposed recently. Most of them model deep fake detection as a binary classification problem using a backbone convolutional neural network (CNN) architecture pretrained for the task. These CNN-based methods have demonstrated very high efficacy in deep fake detection with the Area under the Curve (AUC) as high as 0.99. However, the performance of these methods degrades significantly when evaluated across datasets. In this paper, we formulate deep fake detection as a hybrid combination of supervised and reinforcement learning (RL) to improve its cross-dataset generalization performance. The proposed method chooses the top-k augmentations for each test sample by an RL agent in an image-specific manner. The classification scores, obtained using CNN, of all the augmentations of each test image are averaged together for final real or fake classification. Through extensive experimental validation, we demonstrate the superiority of our method over existing published research in cross-dataset generalization of deep fake detectors, thus obtaining state-of-the-art performance.

关键词： Deepfakes computer vision conferences Detectors Reinforcement learning computer architecture pattern recognition

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Unsupervised Salient Object Detection with Spectral Cluster Voting

Unsupervised Salient Object Detection with Spectral Cluster ...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Shin, Gyungin Albanie, Samuel Xie, Weidi Univ Oxford Visual Geometry Grp Oxford England Univ Cambridge Dept Engn Cambridge England Shanghai Jiao Tong Univ Shanghai Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In this paper, we tackle the challenging task of unsupervised salient object detection (SOD) by leveraging spectral clustering on self-supervised features. We make the following contributions: (i) We revisit spectral clustering and demonstrate its potential to group the pixels of salient objects across various self-supervised features, e.g., MoCov2, SwAV, and DINO;(ii) Given mask proposals from multiple applications of spectral clustering on image features computed from different self-supervised models, we propose a simple but effective winner-takes-all voting mechanism for selecting the salient masks, leveraging object priors based on framing and distinctiveness;(iii) Using the selected object segmentation as pseudo groundtruth masks, we train a salient object detector, termed SELF-MASK, which outperforms prior approaches on three unsupervised SOD benchmarks.

关键词： conferences Object detection Object segmentation Detectors Benchmark testing Feature extraction pattern recognition

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Wearable ImageNet: Synthesizing Tileable Textures via Dataset Distillation

Wearable ImageNet: Synthesizing Tileable Textures via Datase...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Cazenavette, George Wang, Tongzhou Torralba, Antonio Efros, Alexei A. Zhu, Jun-Yan Carnegie Mellon Univ Pittsburgh PA 15213 USA MIT Cambridge MA 02139 USA Univ Calif Berkeley Berkeley CA USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Recent methods for Dataset Distillation are able to take in a large set of images of a specific class (e.g., from ImageNet) and synthesize a single image, such that a classifier trained on that image could perform similarly to one trained on the original dataset. It was noticed that the resulting "distilled images" are often quite visually pleasing. In this paper, we describe a simple method for generating tileable distilled textures by sampling random crops from a toroidal canvas of synthetic pixels while enforcing that all such crops serve as effective distilled training data. Such distilled textures not only summarize a given image category in a visually interesting way, but also allow for generation of infinite texture patterns suitable for printing on fabric, clothing, etc. This paper might be just the first step in making the ImageNet dataset into a fashion statement.

关键词： Printing computer vision Wearable computers conferences Clothing Crops Training data

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