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检索条件"任意字段=2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2024"

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GRIB: Combining Global Reception and Inductive Bias For Human Segmentation and Matting

GRIB: Combining Global Reception and Inductive Bias For Huma...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Yezhi Shen Weichen Xu Qian Lin Jan P. Allebach Fengqing Zhu Purdue University West Lafayette IN USA HP Inc Palo Alto CA USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Human video segmentation and matting are challenging computer vision tasks, with many applications such as background replacement or background editing. Numerous methods have been proposed for human segmentation and matting in either portrait or first-person view videos. In this paper, we propose a real-time network that performs first-person view hand and manipulated object segmentation as well as second-person view human video matting. We introduce a global reception inductive bias block in the network’s encoder that aggregates the pixel features at short, medium, and long ranges. Furthermore, we propose a multi-target optimization method that fully leverages segmentation and matting labels to accelerate training. Our model outperforms existing real-time methods by achieving 93.9% mIoU on HP-Portrait, 95.1% mIoU on VideoMatte as well as 72.7% mIoU on EgoHOS datasets and achieves faster runtime.

关键词： Training computer vision Accuracy Runtime Optimization methods Object segmentation Streaming media

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Finding AI-Generated Faces in the Wild

Finding AI-Generated Faces in the Wild

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Gonzalo J. Aniano Porcile Jack Gindi Shivansh Mundra James R. Verbus Hany Farid LinkedIn Sunnyvale CA USA University of California Berkeley Berkeley CA USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

AI-based image generation has continued to rapidly improve, producing increasingly more realistic images with fewer obvious visual flaws. AI-generated images are being used to create fake online profiles which in turn are being used for spam, fraud, and disinformation campaigns. As the general problem of detecting any type of manipulated or synthesized content is receiving increasing attention, here we focus on a more narrow task of distinguishing a real face from an AI-generated face. This is particularly applicable when tackling inauthentic online accounts with a fake user profile photo. We show that by focusing on only faces, a more resilient and general-purpose artifact can be detected that allows for the detection of AI-generated faces from a variety of GAN- and diffusion-based synthesis engines, and across image resolutions (as low as 128 × 128 pixels) and qualities.

关键词： Visualization computer vision Image resolution Image synthesis Face recognition conferences Focusing

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Emotion recognition Using Transformers with Random Masking

Emotion Recognition Using Transformers with Random Masking

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Seongjae Min Junseok Yang Sejoon Lim Kookmin University Korea

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In recent years, deep learning has achieved innovative advancements in various fields, including the analysis of human emotions and behaviors. Initiatives such as the Affective Behavior Analysis in-the-wild (ABAW) competition have been particularly instrumental in driving research in this area by providing diverse and challenging datasets that enable precise evaluation of complex emotional states. This study leverages the vision Transformer (ViT) and Transformer models to focus on the estimation of Valence-Arousal (VA), which signifies the positivity and intensity of emotions, recognition of various facial expressions, and detection of Action Units (AU) representing fundamental muscle movements. This approach transcends traditional Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) based methods, proposing a new Transformer-based framework that maximizes the understanding of temporal and spatial features. The core contributions of this research include the introduction of a learning technique through random frame masking and the application of Focal loss adapted for imbalanced data, enhancing the accuracy and applicability of emotion and behavior analysis in real-world settings. This approach is expected to contribute to the advancement of emotional computing and deep learning methodologies.

关键词： Deep learning Emotion recognition Gold computer vision Computational modeling Estimation Transformer cores

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Wiki-LLaVA: Hierarchical Retrieval-Augmented Generation for Multimodal LLMs

Wiki-LLaVA: Hierarchical Retrieval-Augmented Generation for ...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Davide Caffagni Federico Cocchi Nicholas Moratelli Sara Sarto Marcella Cornia Lorenzo Baraldi Rita Cucchiara University of Modena and Reggio Emilia Italy University of Pisa Italy IIT-CNR Italy

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Multimodal LLMs are the natural evolution of LLMs, and enlarge their capabilities so as to work beyond the pure textual modality. As research is being carried out to design novel architectures and vision-and-language adapters, in this paper we concentrate on endowing such models with the capability of answering questions that require external knowledge. Our approach, termed Wiki-LLaVA, aims at integrating an external knowledge source of multimodal documents, which is accessed through a hierarchical retrieval pipeline. Relevant passages, using this approach, are retrieved from the external knowledge source and employed as additional context for the LLM, augmenting the effectiveness and precision of generated dialogues. We conduct extensive experiments on datasets tailored for visual question answering with external data and demonstrate the appropriateness of our approach.

关键词： Visualization computer vision Adaptation models conferences Pipelines computer architecture Question answering (information retrieval)

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Multi-scale Attention-Based Inclination Angles Estimation for Panoramic Camera

Multi-scale Attention-Based Inclination Angles Estimation fo...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Yuhao Shan Heyu Chen Jiaying Zhang Shigang Li Jianfeng Li Southwest University Chongqing China Hiroshima City University Hiroshima Japan

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Images taken by panoramic cameras in the upright posture can give viewers a better sense and make the downstream panoramic image-based computer vision tasks easier. To estimate the inclination angles of panoramic camera, we proposed a simple but elegant panoramic image-based network, which combines the advantages of geometry-based and deep-learning-based methods. First, a backbone network with five down-sampling layers is designed to focus on the local distortion features. Then, since non-upright panoramic images have highly uniform geometric distortion for the same camera inclination angles, a multi-scale attention module is proposed for the first time, which can weigh each pixel on the feature maps of the backbone network and allows the network to focus on the global and shallow geometric features. Moreover, apart from angle loss, pixel-level image loss is introduced in our network for the inclination angles estimation task to allow the network to compensate for pixel deviations during training. The experiments show that our method overcomes other leading state-of-the-art methods in this field.

关键词： Training computer vision conferences Estimation Cameras Distortion pattern recognition

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Contactless Respiratory Rate Monitoring For ICU Patients Based On Unsupervised Learning

Contactless Respiratory Rate Monitoring For ICU Patients Bas...

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Liu, Zimeng Huang, Bin Lin, Chun-Liang Wu, Chieh-Liang Zhao, Changchen Chao, Wen-Cheng Wu, Yu-Cheng Zheng, Yadan Wang, Zhiru Beihang University Hangzhou Innovataion Institute China Beihang University School of Automation Science and Electrical Engineering China National Chung Hsing University College of Electrical Engineering and Computer Science Taiwan Taichung Veterans General Hospital Department of Critical Care Medicine Taiwan National Chung Hsing University College of Medicine Department of Post-Baccalaureate Medicine Taiwan National Chung Hsing University Big Data Center Taiwan Beijing University of Posts and Telecommunications Computer Science School China Beijing Foreign Studies University Graduate School of Translation and Interpretation China

ISBN: (纸本)9798350302493

Recently, the task of contactless physiological signal monitoring based on deep learning technologies has attracted a large number of scholars. However, few studies focus on the application of real-world scenarios, especially in clinical medicine scenes. In this paper, a novel video-based contactless respiratory rate measurement algorithm is developed for the Intensive Care Unit (ICU) patients. Firstly, a large-scale clinical real-world database towards ICU patient is collected in this study. Then, based on the dataset, the unsupervised learning is first introduced to extract the respiration waveform from the chest area of patients. Lastly, a respiratory rate estimator based on neural networks is proposed and trained on a periodical physiological signal simulation dataset, and utilizes the transfer learning technique to extract the respiratory rate from only 10-second respiration waveform. We obtained estimated respiratory rate with an MAE of 2.8 breaths/min and an STD of 3.0 breaths/min against the reference value computed from the specialized medical device. Extensive experiments demonstrate that our proposed methods achieve competitive results over state-of-the-art (SOTA) method in the real-world scenario. © 2023 ieee.

关键词： Intensive care units

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An analysis of best-practice strategies for replay and rehearsal in continual learning

An analysis of best-practice strategies for replay and rehea...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Alexander Krawczyk Alexander Gepperth Applied Computer Science Department University of Applied Sciences Fulda Fulda Germany

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This study is in the context of class-incremental continual learning using replay, which has seen notable progress in recent years, fueled by concepts like conditional, latent or maximally interfered replay. However, there are many design choices to take when it comes to implementing replay strategies, with potentially very different outcomes in the various class-incremental scenarios. Some of the obvious design choices in replay are the use of experience replay (ER), the use of different generators like GANs –vs– VAEs for generative replay, or whether to re-initialize generators after each task. For replay strategies in general, it is an open question how many samples to generate for each new task, and what weights to give generated and new samples in the loss. On top of this, there are many possible CL evaluation protocols differing in the amount of tasks, the balancing of tasks or fundamental complexity (e.g., MNIST -vs- latent CIFAR/SVHN), and thus few generic conclusions about best practices for replay/rehearsal have found consensus in the literature. This study aims at establishing such best-practices by conducting an extensive set of representative replay experiments.

关键词： Continuing education computer vision Protocols conferences Generators pattern recognition Complexity theory

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Event-based Ball Spin Estimation in Sports

Event-based Ball Spin Estimation in Sports

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Takuya Nakabayashi Kyota Higa Masahiro Yamaguchi Ryo Fujiwara Hideo Saito Keio University Yokohama Japan NEC Corporation Kawasaki Japan

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Ball spin estimation in sports is important for analyzing the game. Since spin is generally too fast to be captured by a conventional camera, a high-speed camera is often used to capture images of the ball and estimate its spin. However, since a high-speed camera is not robust to changes in the lighting conditions, it is difficult to estimate spin in some environments. To solve these problems, this paper proposes a new method for ball spin estimation using an event camera. An event camera is a sensor inspired by the visual system of animals, which outputs the brightness changes in a scene. Event cameras have advantages such as high temporal resolution and high dynamic range, and can accurately capture the motion of a fast-spinning ball in various lighting conditions. Experimental results in a synthesized dataset showed that the proposed method can stably estimate spin up to 500 rps. It is also confirmed that the proposed method can estimate spin in the data obtained from actual sports games.

关键词： Estimation Lighting Games Visual systems Cameras Vectors pattern recognition

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BiMAE - A Bimodal Masked Autoencoder Architecture for Single-Label Hyperspectral Image Classification

BiMAE - A Bimodal Masked Autoencoder Architecture for Single...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Maksim Kukushkin Martin Bogdan Thomas Schmid Leipzig University Leipzig Martin Luther University Halle-Wittenberg Halle (Saale)

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Hyperspectral imaging offers manifold opportunities for applications that may not, or only partially, be achieved within the visual spectrum. Our paper presents a novel approach for Single-Label Hyperspectral Image Classification, demonstrated through the example of a key challenge faced by agricultural seed producers: seed purity testing. We employ Self-Supervised Learning and Masked Image Modeling techniques to tackle this task. Recognizing the challenges and costs associated with acquiring hyperspectral data, we aim to develop a versatile method capable of working with visible, arbitrary combinations of spectral bands (multispectral data) and hyperspectral sensor data. By integrating RGB and hyperspectral data, we leverage the detailed spatial information from RGB images and the rich spectral information from hyperspectral data to enhance the accuracy of seed classification. Through evaluations in various real-life scenarios, we demonstrate the flexibility, scalability, and efficiency of our approach.

关键词： Manifolds Visualization Costs Scalability conferences Self-supervised learning pattern recognition

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Temporal surface frame anomalies for deepfake video detection

Temporal surface frame anomalies for deepfake video detectio...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Andrea Ciamarra Roberto Caldelli Alberto Del Bimbo University of Florence Florence Italy Universitas Mercatorum Rome Italy CNIT Florence Italy

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Looking at a video sequence where a foreground person is represented is not as time ago anymore. Deepfakes have revolutionized our way to watch at such contents and nowadays we are more often used to wonder if what we are seeing is real or is just a mystification. In this context of generalized disinformation, the need for reliable solutions to help common users, and not only, to make an assessment on this kind of video sequences is strongly upcoming. In this paper, a novel approach which leverages on temporal surface frame anomalies in order to reveal deepfake videos is introduced. The method searches for possible discrepancies, induced by deepfake manipulation, in the surfaces belonging to the captured scene and in their evolution along the temporal axis. These features are used as input of a pipeline based on deep neural networks to perform a binary assessment on the video itself. Experimental results witness that such a methodology can achieve significant performance in terms of detection accuracy.

关键词： Deepfakes Accuracy Instruments Video sequences Pipelines Watches pattern recognition

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