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共 8962 条记录，以下是321-330 订阅

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SpiderNet: Hybrid Differentiable-Evolutionary Architecture Search via Train-Free Metrics

SpiderNet: Hybrid Differentiable-Evolutionary Architecture S...

引用

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

作者： Geada, Rob McGough, Andrew Stephen Red Hat Newcastle Upon Tyne Tyne & Wear England Newcastle Univ Newcastle Upon Tyne Tyne & Wear England

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Neural Architecture Search (NAS) algorithms are intended to remove the burden of manual neural network design, and have shown to be capable of designing excellent models for a variety of well-known problems. However, these algorithms require a variety of design parameters in the form of user configuration or hard-coded decisions which limit the variety of networks that can be discovered. This means that NAS algorithms do not eliminate model design tuning, they instead merely shift the burden of where that tuning needs to be applied. In this paper, we present SpiderNet, a hybrid differentiable-evolutionary and hardware-aware algorithm that rapidly and efficiently produces state-of-the-art networks. More importantly, SpiderNet is a proof-of-concept of a minimally-configured NAS algorithm;the majority of design choices seen in other algorithms are incorporated into SpiderNet's dynamically-evolving search space, minimizing the number of user choices to just two: reduction cell count and initial channel count. SpiderNet produces models highly-competitive with the state-of-the-art, and outperforms random search in accuracy, runtime, memory size, and parameter count.

关键词： Measurement Runtime Heuristic algorithms Microprocessors Neural networks computer architecture Manuals

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Few-Shot Supervised Prototype Alignment for Pedestrian Detection on Fisheye Images

Few-Shot Supervised Prototype Alignment for Pedestrian Detec...

引用

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

作者： Wiedemer, Thaddaeus Wolf, Stefan Schumann, Arne Ma, Kaisheng Beyerer, Juergen Tsinghua Univ Beijing Peoples R China Karlsruhe Inst Technol Karlsruhe Germany Fraunhofer IOSB Karlsruhe Germany Fraunhofer Ctr Machine Learning St Augustin Germany

ISBN: (纸本)9781665487399

Complete and pre-trained models are readily available for download for object detection and can perform well on datasets containing everyday images. Domain adaptation is used to transfer models to more specific datasets with characteristics not present in pre-training. We propose the novel adaptation setting of pedestrian detection in fisheye images, where target samples are scarce but annotated. Our setting provides interesting new challenges for adaptation due to global perspective changes and geometric distortions not found in existing adaptation tasks. To this end, we introduce loss coupling for unsupervised adversarial adaptation and boost prototype-based adaptation with ground-truth information. We additionally propose a novel supervised adaptation head for features in the bounding box regressor. Our method leads to more stable adversarial training and outperforms supervised and unsupervised baselines. Our method requires half the amount of training samples for small datasets to achieve the same performance as supervised fine-tuning.

关键词： Training Couplings Adaptation models Schedules Surveillance Prototypes computer architecture

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The multi-modal universe of fast-fashion: the Visuelle 2.0 benchmark

The multi-modal universe of fast-fashion: the Visuelle 2.0 b...

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

作者： Skenderi, Geri Joppi, Christian Denitto, Matteo Scarpa, Berniero Cristani, Marco Univ Verona Verona Italy Humatics Srl Verona Italy Nuna Lie Srl Rome Italy

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We present Visuelle 2.0, the first dataset useful for facing diverse prediction problems that a fast-fashion company has to manage routinely. Furthermore, we demonstrate how the use of computer vision is substantial in this scenario. Visuelle 2.0 contains data for 6 seasons / 5355 clothing products of Nuna Lie(1), a famous Italian company with hundreds of shops located in different areas within the country. In particular, we focus on a specific prediction problem, namely short-observation new product sale forecasting (SO-fore). SO-fore assumes that the season has started and a set of new products is on the shelves of the different stores. The goal is to forecast the sales for a particular horizon, given a short, available past (few weeks), since no earlier statistics are available. To be successful, SO-fore approaches should capture this short past and exploit other modalities or exogenous data. To these aims, Visuelle 2.0 is equipped with disaggregated data at the item-shop level and multi-modal information for each clothing item, allowing computer vision approaches to come into play. The main message that we deliver is that the use of image data with deep networks boosts performances obtained when using the time series in long-term forecasting scenarios, ameliorating the WAPE by 8.2% and the MAE by 7.7%. The dataset is available at : https://***/forecasting/visuelle.

关键词： computer vision conferences Clothing Time series analysis Companies Benchmark testing pattern recognition

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All Keypoints You Need: Detecting Arbitrary Keypoints on the Body of Triple, High, and Long Jump Athletes

All Keypoints You Need: Detecting Arbitrary Keypoints on the...

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2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Ludwig, Katja Lorenz, Julian Schön, Robin Lienhart, Rainer University of Augsburg Machine Learning and Computer Vision Germany

ISBN: (纸本)9798350302493

Performance analyses based on videos are commonly used by coaches of athletes in various sports disciplines. In individual sports, these analyses mainly comprise the body posture. This paper focuses on the disciplines of triple, high, and long jump, which require fine-grained locations of the athlete's body. Typical human pose estimation datasets provide only a very limited set of keypoints, which is not sufficient in this case. Therefore, we propose a method to detect arbitrary keypoints on the whole body of the athlete by leveraging the limited set of annotated keypoints and auto-generated segmentation masks of body parts. Evaluations show that our model is capable of detecting keypoints on the head, torso, hands, feet, arms, and legs, including also bent elbows and knees. We analyze and compare different techniques to encode desired keypoints as the model's input and their embedding for the Transformer backbone. © 2023 ieee.

关键词： Sports

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Optimising rPPG Signal Extraction by Exploiting Facial Surface Orientation

Optimising rPPG Signal Extraction by Exploiting Facial Surfa...

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

作者： Wong, Kwan Long Chin, Jing Wei Chan, Tsz Tai Odinaev, Ismoil Suhartono, Kristian Kang Tianqu So, Richard H. Y. Hong Kong Univ Sci & Technol Hong Kong Peoples R China PanopticAI Ltd Hong Kong Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Remote photoplethysmography (rPPG) is a contactless method to measure human vital signs by detecting subtle skin color changes through a camera. Although many studies have used region of interest (ROI) selection tools to improve rPPG signal extraction, no study has investigated the influence of the ROI's surface orientation. We propose a novel 'angle map' representation of the face to study the effects of the surface orientation on the extracted rPPG signal. The angle map is generated by mapping each facial pixel to an angle of reflection (angle between the skin surface and the camera) calculated from the surface normal of the facial landmarks and the camera axis. Our results show that surface orientation significantly affects the correlation between the extracted rPPG signal and ground truth blood volume pulse (BVP). Regions with small angles of reflection contained stronger signals, which explains why areas near the cheeks and forehead are often chosen for rPPG signal extraction. Moreover, we applied a thresholding method to the angle map and demonstrated its potential for dynamic ROI selection, thereby optimising the rPPG signal extraction process.

关键词： computer vision Forehead Correlation Face recognition conferences Cameras Photoplethysmography

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HR-STAN: High-Resolution Spatio-Temporal Attention Network for 3D Human Motion Prediction

HR-STAN: High-Resolution Spatio-Temporal Attention Network f...

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

作者： Medjaouri, Omar Desai, Kevin Univ Texas San Antonio San Antonio TX 78249 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

3D human motion prediction requires making sense of the complex spatio-temporal dynamics which underpin human motion to make highly accurate predictions. Part of this complexity is due to the trade-off between long-term (>400ms) and short-term predictions (<400ms) which require different levels of granularity to observe patterns. Several works have explored methods of improving long-term prediction performance by utilizing longer motion histories but this typically comes at the cost of very short-term (<200ms) performance. Inspired by high-resolution network architectures, we propose a novel high-resolution spatio-temporal attention network (HR-STAN) which leverages parallel feature branches and dilated convolutions to observe human motion at different scales. Furthermore, we augment this architecture with split spatial and temporal attention mechanisms to efficiently capture spatio-temporal dependencies within a given motion. We evaluate the ability of our HR-STAN architecture at incorporating long-term motion histories while producing short-term predictions and show that it improves over several state-of-the-art methods on both the AMASS and Human3.6M benchmarks.

关键词： computer vision Three-dimensional displays Costs conferences Dynamics computer architecture Predictive models

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Localized Shortcut Removal

Localized Shortcut Removal

引用

2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Müller, Nicolas M. Jacobs, Jochen Williams, Jennifer Böttinger, Konstantin Fraunhofer AISEC Germany TU Munich Germany University of Southampton United Kingdom

ISBN: (纸本)9798350302493

Machine learning is a data-driven field, and the quality of the underlying datasets plays a crucial role in learning success. However, high performance on held-out test data does not necessarily indicate that a model generalizes or learns anything meaningful. This is often due to the existence of machine learning shortcuts - features in the data that are predictive but unrelated to the problem at hand. To address this issue for datasets where the shortcuts are smaller and more localized than true features, we propose a novel approach to detect and remove them. We use an adversarially trained lens to detect and eliminate highly predictive but semantically unconnected clues in images. In our experiments on both synthetic and real-world data, we show that our proposed approach reliably identifies and neutralizes such shortcuts without causing degradation of model performance on clean data. We believe that our approach can lead to more meaningful and generalizable machine learning models, especially in scenarios where the quality of the underlying datasets is crucial. © 2023 ieee.

关键词： computer vision

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Proceedings - 2024 ieee Winter conference on Applications of computer vision workshops, WACVW 2024

Proceedings - 2024 IEEE Winter Conference on Applications of...

引用

2024 ieee/CVF Winter conference on Applications of computer vision workshops, WACVW 2024

ISBN: (纸本)9798350370287

The proceedings contain 123 papers. The topics discussed include: the SARFish dataset and challenge;NORPPA: NOvel ringed seal re-identification by pelage pattern aggregation;multiple toddler tracking in indoor videos;challenges in video-based infant action recognition: a critical examination of the state of the art;KABR: in-situ dataset for kenyan animal behavior recognition from drone videos;the hitchhiker's guide to endangered species pose estimation;efficient domain adaptation via generative prior for 3D infant pose estimation;dynamic gaussian splatting from markerless motion capture reconstruct infants movements;neural texture puppeteer: a framework for neural geometry and texture rendering of articulated shapes, enabling re-identification at interactive speed;and DigiDogs: single-view 3D pose estimation of dogs using synthetic training data.

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A Modular Multimodal Architecture for Gaze Target Prediction: Application to Privacy-Sensitive Settings

A Modular Multimodal Architecture for Gaze Target Prediction...

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

作者： Gupta, Anshul Tafasca, Samy Odobez, Jean-Marc Idiap Res Inst Martigny Switzerland Ecole Polytech Fed Lausanne Lausanne Switzerland

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Predicting where a person is looking is a complex task, requiring to understand not only the person's gaze and scene content, but also the 3D scene structure and the person's situation (are they manipulating? interacting or observing others? attentive?) to detect obstructions in the line of sight or apply attention priors that humans typically have when observing others. In this paper, we hypothesize that identifying and leveraging such priors can be better achieved through the exploitation of explicitly derived multimodal cues such as depth and pose. We thus propose a modular multimodal architecture allowing to combine these cues using an attention mechanism. The architecture can naturally be exploited in privacy-sensitive situations such as surveillance and health, where personally identifiable information cannot be released. We perform extensive experiments on the GazeFollow and VideoAttentionTarget public datasets, obtaining state-of-the-art performance and demonstrating very competitive results in the privacy setting case. (1)

关键词： Three-dimensional displays Fuses Surveillance computer architecture Predictive models Skeleton pattern recognition

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A Two-Stage Shake-Shake Network for Long-Tailed recognition of SAR Aerial View Objects

A Two-Stage Shake-Shake Network for Long-Tailed Recognition ...

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

作者： Li, Gongzhe Pan, Linpeng Qiu, Linwei Tan, Zhiwen Xie, Fengying Zhang, Haopeng Beihang Univ Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Synthetic Aperture Radar (SAR) has received more attention due to its complementary superiority on capturing significant information in the remote sensing area. However, for an Aerial View Object Classification (AVOC) task, SAR images still suffer from the long-tailed distribution of the aerial view objects. This disparity limit the performance of classification methods, especially for the data-sensitive deep learning models. In this paper, we propose a two-stage shake-shake network to tackle the long-tailed learning problem. Specifically, it decouples the learning procedure into the representation learning stage and the classification learning stage. Moreover, we apply the test time augmentation (TTA) and the classification with alternating normalization (CAN) to improve the accuracy. In the PBVS 1 2022 Multi-modal Aerial View Object Classification Challenge Track 1, our method achieves 21.82% and 27.97% accuracy in the development phase and testing phase respectively, which wins the top-tier among all the participants.

关键词： Training Representation learning Feature extraction Radar tracking Radar polarimetry pattern recognition Task analysis

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