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检索条件"任意字段=Conference on Computer Vision and Pattern Recognition"

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Towards computer vision and Deep Learning Facilitated Pollination Monitoring for Agriculture

Towards Computer Vision and Deep Learning Facilitated Pollin...

引用

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

作者： Ratnayake, Malika Nisal Dyer, Adrian G. Dorin, Alan Monash Univ Fac Informat Technol Clayton Vic Australia RMIT Univ Sch Media & Commun Melbourne Vic Australia Monash Univ Dept Physiol Clayton Vic Australia

ISBN: (纸本)9781665448994

Globally, pollinators affect 35% of agricultural land and play a key role in food production. Consequently, monitoring is useful to understand the contribution insects make towards crop pollination. Traditional sampling techniques used in insect monitoring have several drawbacks, including that they are labour intensive and potentially unreliable. Some of these drawbacks may be overcome using computer vision and deep learning-based approaches to automate pollination monitoring. In this paper, we present a pipeline for computer vision-based pollination monitoring and propose a novel algorithm, Polytrack, that tracks multiple insects simultaneously in complex agricultural environments. Our algorithm uses deep learning and foreground/background segmentation to detect and track insects. We achieved precision and recall rates of 0.975 and 0.972 respectively when monitoring honeybees foraging in our test sites within the polytunnels of an industrial strawberry farm. Polytrack includes a flower identification module to automate collection of insect-flower interaction data, and a low-resolution processing mode that reduces computational demands placed on the processor to bring the software towards the requirements of low-powered monitoring hardware.

关键词： Deep learning computer vision Visualization Insects Pipelines Production Agriculture

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Does Image Anonymization Impact computer vision Training?

Does Image Anonymization Impact Computer Vision Training?

引用

IEEE computer Society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Håkon Hukkelås Frank Lindseth Deparment of Computer Science Norwegian University of Science and Technology

Image anonymization is widely adapted in practice to comply with privacy regulations in many regions. However, anonymization often degrades the quality of the data, reducing its utility for computer vision development. In this paper, we investigate the impact of image anonymization for training computer vision models on key computer vision tasks (detection, instance segmentation, and pose estimation). Specifically, we benchmark the recognition drop on common detection datasets, where we evaluate both traditional and realistic anonymization for faces and full bodies. Our comprehensive experiments reflect that traditional image anonymization substantially impacts final model performance, particularly when anonymizing the full body. Furthermore, we find that realistic anonymization can mitigate this decrease in performance, where our experiments reflect a minimal performance drop for face anonymization. Our study demonstrates that realistic anonymization can enable privacy-preserving computer vision development with minimal performance degradation across a range of important computer vision benchmarks.

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Few-shot Open-set recognition by Transformation Consistency

Few-shot Open-set Recognition by Transformation Consistency

引用

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

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

ISBN: (纸本)9781665445092

In this paper, we attack a few-shot open-set recognition (FSOSR) problem, which is a combination of few-shot learning (FSL) and open-set recognition (OSR). It aims to quickly adapt a model to a given small set of labeled samples while rejecting unseen class samples. Since OSR requires rich data and FSL considers closed-set classification, existing OSR and FSL methods show poor performances in solving FSOSR problems. The previous FSOSR method follows the pseudo-unseen class sample-based methods, which collect pseudo-unseen samples from the other dataset or synthesize samples to model unseen class representations. However, this approach is heavily dependent on the composition of the pseudo samples. In this paper, we propose a novel unknown class sample detector, named SnaTCHer, that does not require pseudo-unseen samples. Based on the transformation consistency, our method measures the difference between the transformed prototypes and a modified prototype set. The modified set is composed by replacing a query feature and its predicted class prototype. SnaTCHer rejects samples with large differences to the transformed prototypes. Our method alters the unseen class distribution estimation problem to a relative feature transformation problem, independent of pseudo-unseen class samples. We investigate our SnaTCHer with various prototype transformation methods and observe that our method consistently improves unseen class sample detection performance without closed-set classification reduction.

关键词： Learning systems computer vision Adaptation models Prototypes Estimation Detectors pattern recognition

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A Real-Time 3D Perception Method with Multi-resolution Voxel Space 6

A Real-Time 3D Perception Method with Multi-resolution Voxel...

引用

6th IEEE International conference on pattern recognition and Artificial Intelligence, PRAI 2023

作者： Zhu, Haowei Shi, Jinlong Wang, Chenggen Lu, Jiawen Jiangsu University of Science and Technology School of Computer Zhenjiang China

ISBN: (纸本)9798350325485

3D perception is the key technology of intelligent equipment to understand the world. We propose an online 3D perception method from RGBD sequences which consists of 3D instance segmentation, camera pose optimization, 3D reconstruction, and refinement modules. Firstly, we perform 3D instance segmentation by mapping and fusing 2D instance segmentation in 3D space. Secondly, we obtain accurate camera poses by adopting a frame-To-frame optimization method and local-To-global strategy. Thirdly, we reconstruct 3D scenes by a multi-resolution implicit structure based on voxels with semantic information. Finally, we render refined implicit surfaces by an improved rendering method. Experiments show that our method has better 3D perception results in a variety of complex environments compared with similar methods. Our code is available: https://***/3D-vision-Lab-of-JUST/Real-Time-3D-Perception. © 2023 IEEE.

关键词： Cameras

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DrivingGaussian: Composite Gaussian Splatting for Surrounding Dynamic Autonomous Driving Scenes

DrivingGaussian: Composite Gaussian Splatting for Surroundin...

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conference on computer vision and pattern recognition (CVPR)

作者： Xiaoyu Zhou Zhiwei Lin Xiaojun Shan Yongtao Wang Deqing Sun Ming-Hsuan Yang Wangxuan Institute of Computer Technology Peking University Google Research University of California Merced

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We present DrivingGaussian, an efficient and effective framework for surrounding dynamic autonomous driving scenes. For complex scenes with moving objects, we first sequentially and progressively model the static background of the entire scene with incremental static 3D Gaussians. We then leverage a composite dynamic Gaussian graph to handle multiple moving objects, individually reconstructing each object and restoring their accurate positions and occlusion relationships within the scene. We further use a LiDAR prior for Gaussian Splatting to reconstruct scenes with greater details and maintain panoramic consistency. DrivingGaussian outperforms existing methods in dynamic driving scene reconstruction and enables photorealistic surround-view synthesis with high-fidelity and multi-camera consistency. Our project page is at: https:/***/VDIGPKU/DrivingGaussian.

关键词： Solid modeling computer vision Three-dimensional displays Laser radar Accuracy pattern recognition Autonomous vehicles

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Towards Quantitative Evaluation Metrics for Image Editing Approaches

Towards Quantitative Evaluation Metrics for Image Editing Ap...

引用

IEEE computer Society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Dana Cohen Hochberg Oron Anschel Alon Shoshan Igor Kviatkovsky Manoj Aggarwal Gérard Medioni Amazon

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In the rapidly evolving field of Generative AI, this work takes initial steps towards establishing a systematic approach for comparing image editing methods. Currently, there is a lack of quantitative metrics for evaluating image editing tasks, with new methods being evaluated mostly qualitatively. Our methodology involves three key components: 1) The creation of a large synthetic dataset using GAN-Control, which enables the generation of ground-truth images for consistent edits across different facial identities; 2) A matching procedure that pairs the edited images with their corresponding ground-truth; and 3) Application of the Perceptual Distance metric to matched pairs. We assessed the effectiveness of our proposed framework through a user study and a set of simulation experiments. Our results indicate that our approach can rank image-editing methods in a way that aligns with human judgment. This research seeks to lay the foundation for a comprehensive evaluation framework for image editing techniques in subsequent studies, initiating a dialogue on this topic.

关键词： Measurement computer vision Systematics Generative AI conferences pattern recognition Synthetic data

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Unveiling the Anomalies in an Ever-Changing World: A Benchmark for Pixel-Level Anomaly Detection in Continual Learning

Unveiling the Anomalies in an Ever-Changing World: A Benchma...

引用

IEEE computer Society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Nikola Bugarin Jovana Bugaric Manuel Barusco Davide Dalle Pezze Gian Antonio Susto University of Padova

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Anomaly Detection is a relevant problem in numerous real-world applications, especially when dealing with images. However, little attention has been paid to the issue of changes over time in the input data distribution, which may cause a significant decrease in performance. In this study, we investigate the problem of Pixel-Level Anomaly Detection in the Continual Learning setting, where new data arrives over time and the goal is to perform well on new and old data. We implement several state-of-the-art techniques to solve the Anomaly Detection problem in the classic setting and adapt them to work in the Continual Learning setting. To validate the approaches, we use a real-world dataset of images with pixel-based anomalies to provide a reliable benchmark and serve as a foundation for further advancements in the field. We provide a comprehensive analysis, discussing which Anomaly Detection methods and which families of approaches seem more suitable for the Continual Learning setting.

关键词： Continuing education computer vision conferences Benchmark testing pattern recognition Reliability Anomaly detection

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Rethinking the Evaluation Protocol of Domain Generalization

Rethinking the Evaluation Protocol of Domain Generalization

引用

conference on computer vision and pattern recognition (CVPR)

作者： Han Yu Xingxuan Zhang Renzhe Xu Jiashuo Liu Yue He Peng Cui Department of Computer Science Tsinghua University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Domain generalization aims to solve the challenge of Out-of-Distribution (OOD) generalization by leveraging common knowledge learnedfrom multiple training domains to generalize to unseen test domains. To accurately evaluate the OOD generalization ability, it is required that test data information is unavailable. However, the current domain generalization protocol may still have potential test data information leakage. This paper examines the risks of test data information leakage from two aspects of the current evaluation protocol: supervised pretraining on ImageNet and oracle model selection. We propose modifications to the current protocol that we should employ self-supervised pretraining or train from scratch instead of employing the current supervised pretraining, and we should use multiple test domains. These would result in a more precise eval-uation of OOD generalization ability. We also rerun the algorithms with the modified protocol and introduce new leaderboards to encourage future research in domain gen-eralization with a fairer comparison.

关键词： Training computer vision Protocols Information leakage Data models pattern recognition

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EyePAD++: A Distillation-based approach for joint Eye Authentication and Presentation Attack Detection using Periocular Images

EyePAD++: A Distillation-based approach for joint Eye Authen...

引用

2022 IEEE/CVF conference on computer vision and pattern recognition, CVPR 2022

作者： Dhar, Prithviraj Kumar, Amit Kaplan, Kirsten Gupta, Khushi Ranjan, Rakesh Chellappa, Rama Johns Hopkins University United States Meta Reality Labs

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

A practical eye authentication (EA) system targeted for edge devices needs to perform authentication and be robust to presentation attacks, all while remaining compute and latency efficient. However, existing eye-based frameworks a) perform authentication and Presentation Attack Detection (PAD) independently and b) involve significant pre-processing steps to extract the iris region. Here, we introduce a joint framework for EA and PAD using periocular images. While a deep Multitask Learning (MTL) network can perform both the tasks, MTL suffers from the forgetting effect since the training datasets for EA and PAD are disjoint. To overcome this, we propose Eye Authentication with PAD (EyePAD), a distillation-based method that trains a single network for EA and PAD while reducing the effect of forgetting. To further improve the EA performance, we introduce a novel approach called EyePAD++ that includes training an MTL network on both EA and PAD data, while distilling the 'versatility' of the EyePAD network through an additional distillation step. Our proposed methods outperform the SOTA in PAD and obtain near-SOTA performance in eye-to-eye verification, without any pre-processing. We also demonstrate the efficacy of EyePAD and EyePAD++ in user-to-user verification with PAD across network backbones and image quality. © 2022 IEEE.

关键词： Training Performance evaluation Image quality Iris Protocols Machine vision Image edge detection

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Extracurricular Learning: Knowledge Transfer Beyond Empirical Distribution

Extracurricular Learning: Knowledge Transfer Beyond Empirica...

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

作者： Pouransari, Hadi Javaheripi, Mojan Sharma, Vinay Tuzel, Oncel Apple Cupertino CA 95014 USA UCSD La Jolla CA USA

ISBN: (纸本)9781665448994

Knowledge distillation has been used to transfer knowledge learned by a sophisticated model (teacher) to a simpler model (student). This technique is widely used to compress model complexity. However, in most applications the compressed student model suffers from an accuracy gap with its teacher. We propose extracurricular learning, a novel knowledge distillation method, that bridges this gap by (1) modeling student and teacher output distributions;(2) sampling examples from an approximation to the underlying data distribution;and (3) matching student and teacher output distributions over this extended set including uncertain samples. We conduct rigorous evaluations on regression and classification tasks and show that compared to the standard knowledge distillation, extracurricular learning reduces the gap by 46 % to 68 %. This leads to major accuracy improvements compared to the empirical risk minimization-based training for various recent neural network architectures: 16% regression error reduction on the MPIIGaze dataset, +3.4% to +9.1% improvement in top-1 classification accuracy on the CIFAR100 dataset, and +2.9% top-1 improvement on the ImageNet dataset.

关键词： Training Uncertainty Neural networks Estimation Data models pattern recognition Task analysis

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