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检索条件"任意字段=2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023"

共 3320 条记录，以下是251-260 订阅

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SAM: Pushing the Limits of Saliency Prediction Models 31

SAM: Pushing the Limits of Saliency Prediction Models

引用

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

作者： Cornia, Marcella Baraldi, Lorenzo Serra, Giuseppe Cmcidara, Rita Univ Modena & Reggio Emilia Modena Italy Univ Udine Udine Italy

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

The prediction of human eye fixations has been recently gaining a lot of attention thanks to the improvements shown by deep architectures. In our work, we go beyond classical feed-forward networks to predict saliency maps and propose a Saliency Attentive Model which incorporates neural attention mechanisms to iteratively refine predictions. Experiments demonstrate that the proposed strategy overcomes by a considerable margin the state of the art on the largest dataset available for saliency prediction. Here, we provide experimental results on other popular saliency datasets to confirm the effectiveness and the generalization capabilities of our model, which enable us to reach the state of the art on all considered datasets.

关键词： Predictive models Visualization Measurement Feature extraction conferences computer architecture Computational modeling

来源：评论

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X-MAN: Explaining multiple sources of anomalies in video

X-MAN: Explaining multiple sources of anomalies in video

引用

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

作者： Szymanowicz, Stanislaw Charles, James Cipolla, Roberto Univ Cambridge Cambridge England

ISBN: (纸本)9781665448994

Our objective is to detect anomalies in video while also automatically explaining the reason behind the detector's response. In a practical sense, explainability is crucial for this task as the required response to an anomaly depends on its nature and severity. However, most leading methods (based on deep neural networks) are not interpretable and hide the decision making process in uninterpretable feature representations. In an effort to tackle this problem we make the following contributions: (1) we show how to build interpretable feature representations suitable for detecting anomalies with state of the art performance, (2) we propose an interpretable probabilistic anomaly detector which can describe the reason behind it's response using high level concepts, (3) we are the first to directly consider object interactions for anomaly detection and (4) we propose a new task of explaining anomalies and release a large dataset for evaluating methods on this task. Our method competes well with the state of the art on public datasets while also providing anomaly explanation based on objects and their interactions.

关键词： Training Deep learning Decision making Detectors Probabilistic logic Feature extraction pattern recognition

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Gated Recurrent Unit-Based RNN for Remote Photoplethysmography Signal Segmentation

Gated Recurrent Unit-Based RNN for Remote Photoplethysmograp...

引用

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

作者： Sabour, Rita Meziati Benezeth, Yannick Univ Bourgogne Franche Comte Dijon France Univ Bourgogne Franche Comte ImViA Lab Dijon France

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Remote Photoplethysmography (rPPG) enables quantifying blood volume variations in the skin tissues from an input video recording, using a regular RGB camera. Obtained pulse signals often contain noisy portions due to motion, leading researchers to put aside a great number of rPPG signals in their studies. In this paper, an approach using a Gated Recurrent Unit-based neural network model in order to identify reliable portions in rPPG signals is proposed. This is done by classifying rPPG signal samples into reliable and unreliable samples. For this purpose, rPPG and electrocardiography signals (ECG) were collected from 11 participants, rPPG signal samples were labeled (ECG was used as ground truth), and data were augmented to reach a total number of 11000 1-minute-long rPPG signals. We developed a model composed of a unidimensional CNN and a Bidirectional GRU (1D-CNN+B-GRU) for this study, and obtained an accuracy rate of 85.88%.

关键词： computer network reliability Neural networks Electrocardiography Logic gates Photoplethysmography Skin pattern recognition

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Deep Image Compression with Latent Optimization and Piece-wise Quantization Approximation

Deep Image Compression with Latent Optimization and Piece-wi...

引用

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

作者： Wu, Yuyang Qi, Zhiyang Zheng, Huiming Tao, Lvfang Gao, Wei Peking Univ Sch Elect & Comp Engn Shenzhen Grad Sch Shenzhen Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9781665448994

Benefit from its capability of learning high-dimensional compact representation from raw data, the auto-encoders are widely used in various tasks of data compression. In particular, for deep image compression, auto-encoders generally take the responsibility of mapping original images to the latent representation to be coded. In this paper, we propose a new framework for deep image compression by devising a loss function for latent optimization, and adopting the differentiable approximation of quantization. In our experiments, both subjective and objective results can confirm the effectiveness of our contributions.

关键词： computer vision Image coding Quantization (signal) conferences Data compression pattern recognition Task analysis

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Combining Magnification and Measurement for Non-Contact Cardiac Monitoring

Combining Magnification and Measurement for Non-Contact Card...

引用

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

作者： Nowara, Ewa M. McDuff, Daniel Veeraraghavan, Ashok Rice Univ Houston TX 77251 USA Microsoft Res Redmond WA USA

ISBN: (纸本)9781665448994

Deep learning approaches currently achieve the state-of-the-art results on camera-based vital signs measurement. One of the main challenges with using neural models for these applications is the lack of sufficiently large and diverse datasets. Limited data increases the chances of overfitting models to the available data which in turn can harm generalization. In this paper, we show that the generalizability of imaging photoplethysmography models can be improved by augmenting the training set with "magnified" videos. These augmentations are specifically designed to reveal useful features for recovering the photoplethysmogram. We show that using augmentations of this form is more effective at improving model robustness than other commonly used data augmentation approaches. We show better within-dataset and especially cross-dataset performance with our proposed data augmentation approach on three publicly available datasets.

关键词： Training Deep learning Photoplethysmography Data models Skin Robustness pattern recognition

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Trusting the computer in computer vision: A Privacy-Affirming Framework 30

Trusting the Computer in Computer Vision: A Privacy-Affirmin...

引用

30th ieee/cvf conference on computer vision and pattern recognition workshops (cvprw)

作者： Chen, Andrew Tzer-Yeu Biglari-Abhari, Morteza Wang, Kevin I-Kai Univ Auckland Dept Elect & Comp Engn Embedded Syst Res Grp Auckland New Zealand

ISBN: (纸本)9781538607336

The use of surveillance cameras continues to increase, ranging from conventional applications such as law enforcement to newer scenarios with looser requirements such as gathering business intelligence. Humans still play an integral part in using and interpreting the footage from these systems, but are also a significant factor in causing unintentional privacy breaches. As computer vision methods continue to improve, we argue in this position paper that system designers should reconsider the role of machines in surveillance, and how automation can be used to help protect privacy. We explore this by discussing the impact of the human-in-the-loop, the potential for using abstraction and distributed computing to further privacy goals, and an approach for determining when video footage should be hidden from human users. We propose that in an ideal surveillance scenario, a privacy-affirming framework causes collected camera footage to be processed by computers directly, and never shown to humans. This implicitly requires humans to establish trust, to believe that computer vision systems can generate sufficiently accurate results without human supervision, so that if information about people must be gathered, unintentional data collection is mitigated as much as possible.

关键词： Estimation Field programmable gate arrays OFDM Modulation Filtration Clocks Signal processing algorithms

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Neural Image Recolorization for Creative Domains

Neural Image Recolorization for Creative Domains

引用

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

作者： Li, Boyi Belongie, Serge Lim, Ser-nam Davis, Abe Cornell Univ Ithaca NY 14853 USA Univ Copenhagen Copenhagen Denmark Meta AI New York NY USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We present a self-supervised approach to recolorization of images from design-oriented domains. Our approach can recolor images based on image exemplars or target color palettes provided by a user. In contrast with previous approaches, our method can reproduce color palettes with luminance distributions that differ significantly from input, and our method is the first palette-based approach to distinguish between recolorings that match reflectance and those that match illumination, making it particularly well-suited to visualizing different aesthetic decisions in design applications. The key to our approach is first to learn latent representations for texture and color in a setting where self-supervision is especially straightforward, and then to learn a mapping to our color representation from input color palettes and scene illumination, which offers a more intuitive space for controlling and exploring recolorization.

关键词： Reflectivity Deep learning Visualization computer vision Image color analysis conferences Lighting

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Scene Representation in Bird's-Eye View from Surrounding Cameras with Transformers

Scene Representation in Bird's-Eye View from Surrounding Cam...

引用

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

作者： Zhao, Yun Zhang, Yu Gong, Zhan Zhu, Hong Inspur Elect Informat Ind Co Ltd Dept AI & HPC Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Scene representation in the bird's-eye-view (BEV) coordinate frame provides a succinct and effective way to understand surrounding environments for autonomous vehicles and robotics. In this work, we present an end-to-end architecture to generate the BEV representation from surrounding cameras. To generate the BEV representation, we propose a transformer-based encoder-decoder structure to translate the image features from different cameras into the BEV frame, which takes advantage of the context information in the individual image and the relationship between images in different views. We perform multiple semantic segmentation tasks using the BEV features. Experimental results show that our model outperforms the competitive baseline [20], which demonstrates the effectiveness and efficiency of our method.

关键词： Image segmentation Image coding Computational modeling Semantics Robot vision systems Predictive models Cameras

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Benchmarking Zero-Shot recognition with vision-Language Models: Challenges on Granularity and Specificity

Benchmarking Zero-Shot Recognition with Vision-Language Mode...

引用

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

作者： Xu, Zhenlin Zhu, Yi Deng, Siqi Mittal, Abhay Chen, Yanbei Wang, Manchen Favaro, Paolo Tighe, Joseph Modolo, Davide AWS AI Labs Seattle WA 98109 USA Boson AI Santa Clara CA 95054 USA Meta Menlo Pk CA USA

ISBN: (纸本)9798350365474

This paper presents novel benchmarks for evaluating vision-language models (VLMs) in zero-shot recognition, focusing on granularity and specificity. Although VLMs excel in tasks like image captioning, they face challenges in open-world settings. Our benchmarks test VLMs' consistency in understanding concepts across semantic granularity levels and their response to varying text specificity. Findings show that VLMs favor moderately fine-grained concepts and struggle with specificity, often misjudging texts that differ from their training data. Extensive evaluations reveal limitations in current VLMs, particularly in distinguishing between correct and subtly incorrect descriptions. While fine-tuning offers some improvements, it doesn't fully address these issues, highlighting the need for VLMs with enhanced generalization capabilities for real-world applications. This study provides insights into VLM limitations and suggests directions for developing more robust models.

关键词： Benchmarking Foundational Models vision and language models Zero-shot recognition

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Self-Supervised Learning of Pose-Informed Latents

Self-Supervised Learning of Pose-Informed Latents

引用

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

作者： Jean, Raphael St-Charles, Pierre-Luc Pirk, Soren Brodeur, Simon Menya Solut Sherbrooke PQ Canada Mila Montreal PQ Canada AMLRT Montreal PQ Canada Google Res Mountain View CA USA

ISBN: (纸本)9781665487399

Siamese network architectures trained for self-supervised instance recognition can learn powerful visual representations that are useful in various tasks. Many such approaches maximize the similarity between representations of augmented images of the same object. In this paper, we depart from traditional self-supervised learning benchmarks by defining a novel methodology for new challenging tasks such as zero shot pose estimation. Our goal is to show that common Siamese networks can effectively be trained on frame pairs from video sequences to generate pose-informed representations. Unlike parallel efforts that focus on introducing new image-space operators for data augmentation, we argue that extending the augmentation strategy by using different frames of a video leads to more powerful representations. To show the effectiveness of this approach, we use the Objectron and UCF101 datasets to learn representations and evaluate them on pose estimation, action recognition, and object re-identification. Furthermore, we carefully validate our method against a number of baselines.

关键词： Training computer vision Visualization Pose estimation Video sequences Self-supervised learning Benchmark testing

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