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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 ...

引用

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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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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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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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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ED-DCFNet: an unsupervised encoder-decoder neural model for event-driven feature extraction and object tracking

ED-DCFNet: an unsupervised encoder-decoder neural model for ...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Raz Ramon Hadar Cohen-Duwek Elishai Ezra Tsur Department of Mathematics and Computer Science Neuro-Biomorphic Engineering Lab (NBEL) The Open University of Israel

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Neuromorphic cameras feature asynchronous event-based pixel-level processing and are particularly useful for object tracking in dynamic environments. Current approaches for feature extraction and optical flow with high-performing hybrid RGB-events vision systems require large computational models and supervised learning, which impose challenges for embedded vision and require annotated datasets. In this work, we propose ED-DCFNet, a small and efficient (< 72k) unsupervised multi-domain learning framework, which extracts events-frames shared features without requiring annotations, with comparable performance. Furthermore, we introduce an open-sourced event and frame-based dataset that captures indoor scenes with various lighting and motion-type conditions in realistic scenarios, which can be used for model building and evaluation. The dataset is available at https://***/NBELab/UnsupervisedTracking.

关键词： Training Neuromorphics Computational modeling Machine vision Supervised learning Feature extraction pattern recognition

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SuperLoRA: Parameter-Efficient Unified Adaptation for Large vision Models

SuperLoRA: Parameter-Efficient Unified Adaptation for Large ...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Xiangyu Chen Jing Liu Ye Wang Pu Perry Wang Matthew Brand Guanghui Wang Toshiaki Koike-Akino University of Kansas Lawrence KS USA Mitsubishi Electric Research Laboratories (MERL) Cambridge MA USA Toronto Metropolitan University Toronto Canada

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Low-rank adaptation (LoRA) and its variants are widely employed in fine-tuning large models, including large language models for natural language processing and diffusion models for computer vision. This paper proposes a generalized framework called SuperLoRA that unifies and extends different LoRA variants, which can be realized under different hyper-parameter settings. Introducing new options with grouping, folding, shuffling, projection, and tensor decomposition, SuperLoRA offers high flexibility and demonstrates superior performance, with up to 10-fold gain in parameter efficiency for transfer learning tasks.

关键词： computer vision Adaptation models Tensors Computational modeling Large language models conferences Transfer learning

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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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AnimalFormer: Multimodal vision Framework for Behavior-based Precision Livestock Farming

AnimalFormer: Multimodal Vision Framework for Behavior-based...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Ahmed Qazi Taha Razzaq Asim Iqbal Tibbling Technologies

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We introduce a multimodal vision framework for precision livestock farming, harnessing the power of GroundingDINO, HQSAM, and ViTPose models. This integrated suite enables comprehensive behavioral analytics from video data without invasive animal tagging. GroundingDINO generates accurate bounding boxes around livestock, while HQSAM segments individual animals within these boxes. ViTPose estimates key body points, facilitating posture and movement analysis. Demonstrated on a sheep dataset with grazing, running, sitting, standing, and walking activities, our framework extracts invaluable insights: activity and grazing patterns, interaction dynamics, and detailed postural evaluations. Applicable across species and video resolutions, this framework revolutionizes noninvasive livestock monitoring for activity detection, counting, health assessments, and posture analyses. It empowers data-driven farm management, optimizing animal welfare and productivity through AI-powered behavioral understanding.

关键词： Productivity Legged locomotion Limiting Tagging Livestock Hardware pattern recognition

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An End-to-End vision Transformer Approach for Image Copy Detection

An End-to-End Vision Transformer Approach for Image Copy Det...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Jiahe Steven Lee Wynne Hsu Mong Li Lee Institute of Data Science National University of Singapore Centre for Trusted Internet and Community National University of Singapore

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Image copy detection is one of the pivotal tools to safeguard online information integrity. The challenge lies in determining whether a query image is an edited copy, which necessitates the identification of candidate source images through a retrieval process. The process requires discriminative features comprising of both global descriptors that are designed to be augmentation-invariant and local descriptors that can capture salient foreground objects to assess whether a query image is an edited copy of some source reference image. This work describes an end-to-end solution that leverage a vision Transformer model to learn such discriminative features and perform implicit matching between the query image and the reference image. Experimental results on two benchmark datasets demonstrate that the proposed solution outperforms state-of-the-art methods. Case studies illustrate the effectiveness of our approach in matching reference images from which the query images have been copy-edited.

关键词： computer vision conferences Benchmark testing Transformers Feature extraction pattern recognition Fake news

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On the Application of Binary Neural Networks in Oblivious Inference

On the Application of Binary Neural Networks in Oblivious In...

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

作者： Samragh, Mohammad Hussain, Siam Zhang, Xinqiao Huang, Ke Koushanfar, Farinaz Univ Calif San Diego La Jolla CA 92093 USA San Diego State Univ San Diego CA 92182 USA

ISBN: (纸本)9781665448994

This paper explores the application of Binary Neural Networks (BNN) in oblivious inference, a service provided by a server to mistrusting clients. Using this service, a client can obtain the inference result on her data by a trained model held by the server without disclosing the data or leaning the model parameters. We make two contributions to this field. First, we devise light-weight cryptographic protocols designed specifically to exploit the unique characteristics of BNNs. Second, we present dynamic exploration of the runtime -accuracy tradeoff of BNNs in a single -shot training process. While previous works trained multiple BNNs with different computational complexities (which is cumbersome due to the slow convergence of BNNs), we train a single BNN that can perform inference under different computational budgets. Compared to CryptFlow2, the state-of-the-art in oblivious inference of non binary DNNs, our approach reaches 2x faster inference at the same accuracy. Compared to XONN, the state-of-the-art in oblivious inference of binary networks, we achieve 2x to llx faster inference while obtaining higher accuracy.

关键词： Training computer vision Data analysis Computational modeling Face recognition Neural networks Data models

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