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检索条件"任意字段=2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2003"

共 6678 条记录，以下是811-820 订阅

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Learning Melanocytic Proliferation Segmentation in Histopathology Images from Imperfect Annotations

Learning Melanocytic Proliferation Segmentation in Histopath...

引用

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

作者： Liu, Kechun Mokhtari, Mojgan Li, Beibin Nofallah, Shima May, Caitlin Chang, Oliver Knezevich, Stevan Elmore, Joann Shapiro, Linda Univ Washington Seattle WA 98195 USA Isfahan Univ Med Sci Esfahan Iran Dermatopathol Northwest Bellevue WA USA VA Puget Sound Seattle WA USA Pathol Associates Inc Clovis CA USA Univ Calif Los Angeles Los Angeles CA USA

ISBN: (纸本)9781665448994

Melanoma is the third most common type of skin cancer and is responsible for the most skin cancer deaths. A diagnosis of melanoma is made by the visual interpretation of tissue sections by a pathologist, a challenging task given the complexity and breadth of melanocytic lesions and the subjective nature of biopsy interpretation. We leverage advances in computer vision to aid melanoma diagnosis by segmenting potential regions of lesions on digital images of whole slide skin biopsies. In this study, we demonstrate a Mask-R-CNN-based segmentation framework for such a purpose. To alleviate the cost of data annotation, we leverage a sparse annotation pipeline. Our model can be trained on sparse and noisy labels and achieves state-of-the-art performance in identifying melanocytic proliferations, producing a segmentation with Dice score 0.719, mIOU 0.740 and overall pixel accuracy 0.927.

关键词： Image segmentation computer vision Visualization Annotations Biopsy Pipelines Melanoma

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Sign Segmentation with Changepoint-Modulated Pseudo-Labelling

Sign Segmentation with Changepoint-Modulated Pseudo-Labellin...

引用

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

作者： Renz, Katrin Stache, Nicolaj C. Fox, Neil Varol, Gul Albanie, Samuel Univ Oxford Visual Geometry Grp Oxford England Univ Heilbronn Heilbronn Germany UCL Deafness Cognit & Language Res Ctr London England Univ Gustave Eiffel CNRS Ecole Ponts LIGM Paris France

ISBN: (纸本)9781665448994

The objective of this work is to find temporal boundaries between signs in continuous sign language. Motivated by the paucity of annotation available for this task, we propose a simple yet effective algorithm to improve segmentation performance on unlabelled signing footage from a domain of interest. We make the following contributions: (1) We motivate and introduce the task of source-free domain adaptation for sign language segmentation, in which labelled source data is available for an initial training phase, but is not available during adaptation. (2) We propose the Changepoint-Modulated Pseudo-Labelling (CMPL) algorithm to leverage cues from abrupt changes in motion-sensitive feature space to improve pseudo-labelling quality for adaptation. (3) We showcase the effectiveness of our approach for category-agnostic sign segmentation, transferring from the BSLCORPUS to the BSL-1K and RWTH-PHOENIX-Weather 2014 datasets, where we outperform the prior state of the art.

关键词： Training Adaptation models Assistive technology Motion segmentation conferences Gesture recognition Data models

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OmniFlow: Human Omnidirectional Optical Flow

OmniFlow: Human Omnidirectional Optical Flow

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

作者： Seidel, Roman Apitzsch, Andre Hirtz, Gangolf Tech Univ Chemnitz Fac Elect Engn & Informat Technol D-09126 Chemnitz Germany

ISBN: (纸本)9781665448994

Optical flow is the motion of a pixel between at least two consecutive video frames and can be estimated through an end-to-end trainable convolutional neural network. To this end, large training datasets are required to improve the accuracy of optical flow estimation. Our paper presents OmniFlow: a new synthetic omnidirectional human optical flow dataset. Based on a rendering engine we create a naturalistic 3D indoor environment with textured rooms, characters, actions, objects, illumination and motion blur where all components of the environment are shuffled during the data capturing process. The simulation has as output rendered images of household activities and the corresponding forward and backward optical flow. To verify the data for training volumetric correspondence networks for optical flow estimation we train different subsets of the data and test on OmniFlow with and without Test-Time-Augmentation. As a result we have generated 23,653 image pairs and corresponding forward and backward optical flow. Our dataset can be downloaded from: https://***/byfs

关键词： Training Three-dimensional displays Lighting Estimation Network architecture Rendering (computer graphics) Indoor environment

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Adaptive Binary-Ternary Quantization

Adaptive Binary-Ternary Quantization

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

作者： Razani, Ryan Morin, Gregoire Sari, Eyyub Nia, Vahid Partovi Huawei Noahs Ark Lab Hong Kong Peoples R China

ISBN: (纸本)9781665448994

Neural network models are resource hungry. It is difficult to deploy such deep networks on devices with limited resources, like smart wearables, cellphones, drones, and autonomous vehicles. Low bit quantization such as binary and ternary quantization is a common approach to alleviate this resource requirements. Ternary quantization provides a more flexible model and outperforms binary quantization in terms of accuracy, however doubles the memory footprint and increases the computational cost. Contrary to these approaches, mixed quantized models allow a trade-off between accuracy and memory footprint. In such models, quantization depth is often chosen manually, or is tuned using a separate optimization routine. The latter requires training a quantized network multiple times. Here, we propose an adaptive combination of binary and ternary quantization, namely Smart Quantization (SQ), in which the quantization depth is modified directly via a regularization function, so that the model is trained only once. Our experimental results show that the proposed method adapts quantization depth successfully while keeping the model accuracy high on MNIST and CIFAR10 benchmarks.

关键词： Training Adaptation models Quantization (signal) Computational modeling Wearable computers Neural networks Speech recognition

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Compact and Effective Representations for Sketch-based Image Retrieval

Compact and Effective Representations for Sketch-based Image...

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

作者： Torres, Pablo Saavedra, Jose M. Univ Chile DCC Av Beauchef 851 Santiago Chile Impresee Inc 600 Calif St San Francisco CA USA

ISBN: (纸本)9781665448994

Sketch-based image retrieval (SBIR) has undergone an increasing interest in the community of computer vision bringing high impact in real applications. For instance, SBIR brings an increased benefit to eCommerce search engines because it allows users to formulate a query just by drawing what they need to buy. However, current methods showing high precision in retrieval work in a high dimensional space, which negatively affects aspects like memory consumption and time processing. Although some authors have also proposed compact representations, these drastically degrade the performance in a low dimension. Therefore in this work, we present different results of evaluating methods for producing compact embeddings in the context of sketch-based image retrieval. Our main interest is in strategies aiming to keep the local structure of the original space. The recent unsupervised local-topology preserving dimension reduction method UMAP fits our requirements and shows outstanding performance, improving even the precision achieved by SOTA methods. We evaluate six methods in two different datasets. We use Flickr15K and eCommerce datasets;the latter is another contribution of this work. We show that UMAP allows us to have feature vectors of 16 bytes improving precision by more than 35%.

关键词： Dimensionality reduction computer vision conferences Image retrieval Memory management Search engines pattern recognition

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MobileHumanPose: Toward real-time 3D human pose estimation in mobile devices

MobileHumanPose: Toward real-time 3D human pose estimation i...

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

作者： Choi, Sangbum Choi, Seokeon Kim, Changick Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (纸本)9781665448994

Currently, 3D pose estimation methods are not compatible with a variety of low computational power devices because of efficiency and accuracy. In this paper, we revisit a pose estimation architecture from a viewpoint of both efficiency and accuracy. We propose a mobile-friendly model, MobileHumanPose, for real-time 3D human pose estimation from a single RGB image. This model consists of the modified MobileNetV2 backbone, a parametric activation function, and the skip concatenation inspired by U-Net. Especially, the skip concatenation structure improves accuracy by propagating richer features with negligible computational power. Our model achieves not only comparable performance to the state-of-the-art models but also has a seven times smaller model size compared to the ResNet-50 based model. In addition, our extra small model reduces inference time by 12.2ms on Galaxy S20 CPU, which is suitable for real-time 3D human pose estimation in mobile applications. The source code is available at: https://***/SangbumChoi/MobileHumanPose.

关键词： Performance evaluation Solid modeling Three-dimensional displays Computational modeling Pose estimation computer architecture Real-time systems

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DVS-OUTLAB: A Neuromorphic Event-Based Long Time Monitoring Dataset for Real-World Outdoor Scenarios

DVS-OUTLAB: A Neuromorphic Event-Based Long Time Monitoring ...

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

作者： Bolten, Tobias Pohle-Froehlich, Regina Toennies, Klaus D. Hsch Niederrhein Inst Pattern Recognit Krefeld Germany Univ Magdeburg Dept Simulat & Graph Magdeburg Germany

ISBN: (纸本)9781665448994

Neuromorphic vision sensors are biologically inspired devices which differ fundamentally from well known frame-based sensors. Even though developments in this research area are increasing, applications that rely entirely on event cameras are still relatively rare. This becomes particularly clear when considering real outdoor scenarios apart from laboratory conditions. One obstacle to the development of event-based vision applications in this context may be the lack of labeled datasets for algorithm development and evaluation. Therefore we describe a recording setting of a DVS-based long time monitoring of an urban public area and provide labeled DVS data that also contain effects of environmental outdoor influences recorded in this process. We also describe the processing chain used for label generation, as well as results from a performed denoising benchmark utilizing various spatio-temporal event stream filters. The dataset contains almost 7 hours of real world outdoor event-data with approximate to 47k labeled regions of interest and can be downloaded at http://***/DVS-OUTLAB/

关键词： Privacy Rain Power demand Neuromorphics Noise reduction Pipelines vision sensors

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On-Orbit Inspection of an Unknown, Tumbling Target using NASA's Astrobee Robotic Free-Flyers

On-Orbit Inspection of an Unknown, Tumbling Target using NAS...

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

作者： Oestreich, Charles Espinoza, Antonio Teran Todd, Jessica Albee, Keenan Linares, Richard MIT Cambridge MA 02139 USA

ISBN: (纸本)9781665448994

Autonomous spacecraft critically depend on on-orbit inspection (i.e., relative navigation and inertial properties estimation) to intercept tumbling debris objects or defunct satellites. This work presents a practical method for on-orbit inspection and demonstrates its performance in simulation using NASA's Astrobee robotic free-flyers. The problem is formulated as a simultaneous localization and mapping task, utilizing IMU data from an observing "chaser" spacecraft and point clouds of the observed "target" spacecraft obtained via a 3D time-of-flight camera. The relative navigation between the chaser and target is solved via a factor graph-based approach. The target's principal axes of inertia are then estimated via a conic fit optimization procedure using a polhode analysis. Simulation results indicate the accuracy of the proposed method in preparation for hardware experiments on the International Space Station.

关键词： Space vehicles Three-dimensional displays Heuristic algorithms Robot vision systems Inspection Extraterrestrial measurements International Space Station

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Supervised Contrastive Learning for Snapshot Spectral Imaging Face Anti-Spoofing

Supervised Contrastive Learning for Snapshot Spectral Imagin...

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ieee computer society conference on computer vision and pattern recognition Workshops (cvprW)

作者： Chuanbiao Song Yan Hong Jun Lan Huijia Zhu Weiqiang Wang Jianfu Zhang Ant Group Qing Yuan Research Institute Shanghai Jiao Tong University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This study reveals a cutting-edge re-balanced contrastive learning strategy aimed at strengthening face anti-spoofing capabilities within facial recognition systems, with a focus on countering the challenges posed by printed photos, and highly realistic silicone or latex masks. Leveraging the HySpeFAS dataset, which benefits from Snapshot Spectral Imaging technology to provide hyperspectral images, our approach harmonizes class-level contrastive learning with data resampling and an innovative real-face oriented reweighting technique. This method effectively mitigates dataset imbalances and reduces identity-related biases. Notably, our strategy achieved an unprecedented 0.0000% Average Classification Error Rate (ACER) on the HySpeFAS dataset, ranking first at the Chalearn Snapshot Spectral Imaging Face Anti-spoofing Challenge on cvpr 2024.

关键词： Training computer vision Error analysis Face recognition conferences Neural networks Imaging

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Low Bandwidth Video-Chat Compression using Deep Generative Models

Low Bandwidth Video-Chat Compression using Deep Generative M...

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

作者： Oquab, Maxime Stock, Pierre Gafni, Oran Haziza, Daniel Xu, Tao Zhang, Peizhao Celebi, Onur Hasson, Yana Labatut, Patrick Bose-Kolanu, Bobo Peyronel, Thibault Couprie, Camille Facebook Menlo Pk CA 94025 USA INRIA Le Chesnay France

ISBN: (纸本)9781665448994

To unlock video chat for hundreds of millions of people hindered by poor connectivity or unaffordable data costs, we propose to authentically reconstruct faces on the receiver's device using facial landmarks extracted at the sender's side and transmitted over the network. In this context, we discuss and evaluate the benefits and disadvantages of several deep adversarial approaches. In particular, we explore quality and bandwidth trade-offs for approaches based on static landmarks, dynamic landmarks or segmentation maps. We design a mobile-compatible architecture based on the first order animation model of Siarohin et al. In addition, we leverage SPADE blocks to refine results in important areas such as the eyes and lips. We compress the networks down to about 3 MB, allowing models to run in real time on iPhone 8 (CPU). This approach enables video calling at a few kbits per second, an order of magnitude lower than currently available alternatives.

关键词： Image segmentation Image coding Lips Bandwidth computer architecture Video compression Streaming media

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