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Motion Matters: Difference-based Multi-scale Learning for Infrared UAV Detection

Motion Matters: Difference-based Multi-scale Learning for In...

引用

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

作者： Ruian He Shili Zhou Ri Cheng Yuqi Sun Weimin Tan Bo Yan School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Shanghai Collaborative Innovation Center of Intelligent Visual Computing Fudan University Shanghai China

Unmanned Aerial Vehicle (UAV) detection in the wild is a challenging task due to the presence of background noise and the varying size of the object. To address these obstacles, we propose a novel learning framework for robust UAV detectors, which we call Difference-based Multi-scale Learning (DML). We argue that motion information matters in UAV detection because of the low recognition in one frame. Our method utilizes the frame difference of multiple previous frames, extracting motion information and blocking background noise. We also fuse multiple spatial-temporal scales for training and inferencing, enabling fusion from different sources. In addition, to better evaluate the performance of UAV detection in different scales, we propose Multi-Scale Average Precision (MSAP) metric to aggregate the detection accuracy over multiple scales. Through extensive experiments, we demonstrate that our proposed approach improves the detection accuracy of baseline models. Notably, we achieve SOTA performance in the 3rd Anti-UAV Challenge, with 2nd place in Track 2 and 4th place in Track 1. 1

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Camera-based Recovery of Cardiovascular Signals from Unconstrained Face Videos using an Attention Network

Camera-based Recovery of Cardiovascular Signals from Unconst...

引用

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

作者： Yogesh Deshpande Surendrabikram Thapa Abhijit Sarkar A. Lynn Abbott Bradley Department of Electrical and Computer Engineering Virginia Tech USA Department of Computer Science Virginia Tech USA Virginia Tech Transportation Institute USA

This paper addresses the problem of recovering the shape morphology of blood volume pulse (BVP) information from a video of a person’s face. Video-based remote plethysmography methods have shown promising results in estimating vital signs such as heart rate and breathing rate. However, recovering the instantaneous pulse rate signals is still a challenge for the community. This is due to the fact that most of the previous methods concentrate on capturing the temporal average of the cardiovascular signals. In contrast, we present an approach in which BVP signals are extracted with a focus on the recovery of the signal shape morphology as a generalized form for the computation of physiological metrics. We also place emphasis on allowing natural movements by the subject. Furthermore, our system is capable of extracting individual BVP instances with sufficient signal detail to facilitate candidate re-identification. These improvements have resulted in part from the incorporation of a robust skin-detection module into the overall imaging-based photoplethysmography (iPPG) framework. We present extensive experimental results using the challenging UBFC-Phys dataset and the well-known COHFACE dataset. The source code is available at https://***/yogeshd21/CVPM-2023-iPPG-Paper.

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Affine-based Deformable Attention and Selective Fusion for Semi-dense Matching

Affine-based Deformable Attention and Selective Fusion for S...

引用

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

作者： Hongkai Chen Zixin Luo Yurun Tian Xuyang Bai Ziyu Wang Lei Zhou Mingmin Zhen Tian Fang David McKinnon Yanghai Tsin Long Quan Apple Inc Hong Kong University of Science and Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Identifying robust and accurate correspondences across images is a fundamental problem in computer vision that enables various downstream tasks. Recent semi-dense matching methods emphasize the effectiveness of fusing relevant cross-view information through Transformer. In this paper, we propose several improvements upon this paradigm. Firstly, we introduce affine-based local attention to model cross-view deformations. Secondly, we present selective fusion to merge local and global messages from cross attention. Apart from network structure, we also identify the importance of enforcing spatial smoothness in loss design, which has been omitted by previous works. Based on these augmentations, our network demonstrate strong matching capacity under different settings. The full version of our network achieves state-of-the-art performance among semi-dense matching methods at a similar cost to LoFTR, while the slim version reaches LoFTR baseline’s performance with only 15% computation cost and 18% parameters.

关键词： Geometry Deformable models computer vision Costs Deformation Shape Fuses

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Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detection

Back to the Feature: Classical 3D Features are (Almost) All ...

引用

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

作者： Eliahu Horwitz Yedid Hoshen School of Computer Science and Engineering The Hebrew University of Jerusalem Israel

Despite significant advances in image anomaly detection and segmentation, few methods use 3D information. We utilize a recently introduced 3D anomaly detection dataset to evaluate whether or not using 3D information is a lost opportunity. First, we present a surprising finding: standard color-only methods outperform all current methods that are explicitly designed to exploit 3D information. This is counter-intuitive as even a simple inspection of the dataset shows that color-only methods are insufficient for images containing geometric anomalies. This motivates the question: how can anomaly detection methods effectively use 3D information? We investigate a range of shape representations including hand-crafted and deep-learning-based; we demonstrate that rotation invariance plays the leading role in the performance. We uncover a simple 3D-only method that beats all recent approaches while not using deep learning, external pre-training datasets, or color information. As the 3D-only method cannot detect color and texture anomalies, we combine it with color-based features, significantly outperforming previous state-of-the-art. Our method, dubbed BTF (Back to the Feature) achieves pixel-wise ROCAUC: 99.3% and PRO: 96.4% on MVTec 3D-AD.

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LSDIR: A Large Scale Dataset for Image Restoration

LSDIR: A Large Scale Dataset for Image Restoration

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

作者： Yawei Li Kai Zhang Jingyun Liang Jiezhang Cao Ce Liu Rui Gong Yulun Zhang Hao Tang Yun Liu Denis Demandolx Rakesh Ranjan Radu Timofte Luc Van Gool Computer Vision Lab ETH Zürich Meta Reality Labs University of Würzburg KU Leuven

The aim of this paper is to propose a large scale dataset for image restoration (LSDIR). Recent work in image restoration has been focused on the design of deep neural networks. The datasets used to train these networks ‘only’ contain some thousands of images, which is still incomparable with the large scale datasets for other vision tasks such as visual recognition and object detection. The small training set limits the performance of image restoration networks. To solve this problem, we collect high-resolution (HR) images from Flickr for image restoration. To ensure the pixel-level quality of the collected dataset, annotators were invited to manually inspect each of the collected image and remove the low-quality ones. The final dataset contains 84,991 high-quality training images, 1,000 validation images, and 1,000 test images. In addition, we showed that the model capacity of large networks could be fully exploited by training on the large scale dataset with significantly increased patch size and prolonged training iterations. The experimental results on image super-resolution (SR), denoising, JPEG deblocking, deblurring, and demosaicking, and real-world SR show that image restoration networks benefit a lot from the large scale dataset.

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RB-Dust - A Reference-based Dataset for vision-based Dust Removal

RB-Dust - A Reference-based Dataset for Vision-based Dust Re...

引用

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

作者： Peter Buckel Timo Oksanen Thomas Dietmueller Baden-Wuerttemberg Cooperative State University (DHBW) Ravensburg Germany Chair of Agrimechatronics Munich Institute of Robotics and Machine Intelligence (MIRMI) Technical University of Munich Germany

Dust in the agricultural landscape is a significant challenge and influences, for example, the environmental perception of autonomous agricultural machines. Image enhancement algorithms can be used to reduce dust. However, these require dusty and dust-free images of the same environment for validation. In fact, to date, there is no dataset that we are aware of that addresses this issue. Therefore, we present the agriscapes RB-Dust dataset, which is named after its purpose of reference-based dust removal. It is not possible to take pictures from the cabin during tillage, as this would cause shifts in the images. Because of this, we built a setup from which it is possible to take images from a stationary position close to the passing tractor. The test setup was based on a half-sided gate through which the tractor could drive. The field tests were carried out on a farm in Bavaria, Germany, during tillage. During the field tests, other parameters such as soil moisture and wind speed were controlled, as these significantly affect dust development. We validated our dataset with contrast enhancement and image dehazing algorithms and analyzed the generalizability from recordings from the moving tractor. Finally, we demonstrate the application of dust removal based on a high-level vision task, such as person classification. Our empirical study confirms the validity of RB-Dust for vision-based dust removal in agriculture.

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UP-NAS: Unified Proxy for Neural Architecture Search

UP-NAS: Unified Proxy for Neural Architecture Search

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

作者： Yi-Cheng Huang Wei-Hua Li Chih-Han Tsou Jun-Cheng Chen Chu-Song Chen National Taiwan University Academia Sinica

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Recently, zero-cost proxies for neural architecture search (NAS) have attracted increasing attention. They allow us to discover top-performing neural networks through architecture scoring without requiring training a very large network (i.e., supernet). Thus, it can save significant computation resources to complete the search. However, to our knowledge, no single proxy works best for different tasks and scenarios. To consolidate the strength of different proxies and to reduce search bias, we propose a unified proxy neural architecture search framework (UP-NAS) which learns a multi-proxy estimator for predicting a unified score by combining multiple zero-cost proxies. The predicted score is then used for an efficient gradient-ascent architecture search in the embedding space of the neural network architectures. Our approach can not only save computational time required for multiple proxies during architecture search but also gain the flexibility to consolidate the existing proxies on different tasks. We conduct experiments on the search spaces of NAS-Bench-201 and DARTS in different datasets. The results demonstrate the effectiveness of the proposed approach. Code is available at https://***/AI-Application-andIntegration-Lab/UP-NAS.

关键词： Training computer vision Codes conferences Neural networks computer architecture pattern recognition

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VLM-PL: Advanced Pseudo Labeling approach for Class Incremental Object Detection via vision-Language Model

VLM-PL: Advanced Pseudo Labeling approach for Class Incremen...

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

作者： Junsu Kim Yunhoe Ku Jihyeon Kim Junuk Cha Seungryul Baek UNIST MODULABS

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In the field of Class Incremental Object Detection (CIOD), creating models that can continuously learn like humans is a major challenge. Pseudo-labeling methods, although initially powerful, struggle with multi-scenario incremental learning due to their tendency to forget past knowledge. To overcome this, we introduce a new approach called vision-Language Model assisted Pseudo-Labeling (VLM-PL). This technique uses vision-Language Model (VLM) to verify the correctness of pseudo ground-truths (GTs) without requiring additional model training. VLM-PL starts by deriving pseudo GTs from a pre-trained detector. Then, we generate custom queries for each pseudo GT using carefully designed prompt templates that combine image and text features. This allows the VLM to classify the correctness through its responses. Furthermore, VLM-PL integrates refined pseudo and real GTs from upcoming training, effectively combining new and old knowledge. Extensive experiments conducted on the Pascal VOC and MS COCO datasets not only highlight VLM-PL’s exceptional performance in multi-scenario but also illuminate its effectiveness in dual-scenario by achieving state-of-the-art results in both.

关键词： Training computer vision Incremental learning conferences Computational modeling Object detection Detectors

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Federated Hyperparameter Optimization through Reward-Based Strategies: Challenges and Insights

Federated Hyperparameter Optimization through Reward-Based S...

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

作者： Krishna Kanth Nakka Ahmed Frikha Ricardo Mendis Xue Jiang Xuebing Zhou Huawei Munich Research Center

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Performing hyperparameter tuning in federated learning is often prohibitively expensive due to the substantial communication overhead associated with training a single configuration, especially with a large hyperparameter search space. To overcome this challenge, recent works explored reward-based approaches to learn a policy distribution over a set of hyperparameter configurations. These approaches enable the concurrent exploration of multiple hyperparameter configurations within a single communication round, thereby accelerating the search *** this paper, we take a deeper look at the reward-based strategies and systematically analyze them, uncovering several issues and challenges associated with their adoption in practice. Furthermore, motivated by the insights from our analysis, we propose an in-depth evaluation of policy distribution with metrics that capture rankings of standalone configurations. We contribute this critical examination and proposed evaluation metrics in order to raise awareness about the challenges and hidden issues that reward-based federated hyperparameter optimization might face and to enable a more rigorous evaluation and therefore a faster progress in this research area. We expect that the identified challenges will serve as inspiration for the development of more robust and hyperparameter-free federated hyperparameter tuning approaches.

关键词： Training computer vision Systematics Sensitivity Federated learning Face recognition conferences

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Exploring Joint Embedding Architectures and Data Augmentations for Self-Supervised Representation Learning in Event-Based vision

Exploring Joint Embedding Architectures and Data Augmentatio...

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

作者： Sami Barchid José Mennesson Chaabane Djéraba Univ. Lille CNRS Centrale Lille UMR 9189 CRIStAL IMT Nord Europe Institut Mines-Télécom Univ. Lille Centre for Digital Systems Lille France

This paper proposes a self-supervised representation learning (SSRL) framework for event-based vision, which leverages various lightweight convolutional neural networks (CNNs) including 2D-, 3D-, and Spiking CNNs. The method uses a joint embedding architecture to maximize the agreement between features extracted from different views of the same event sequence. Popular event data augmentation techniques are employed to design an efficient augmentation policy for event-based SSRL, and we provide novel data augmentation methods to enhance the pretraining pipeline. Given the novelty of SSRL for event-based vision, we elaborate standard evaluation protocols and use them to evaluate our approach. Our study demonstrates that pretrained CNNs acquire effective and transferable features, enabling them to achieve competitive performance in object or action recognition across various commonly used event-based datasets, even in a low-data regime. This paper also conducts an experimental analysis of the extracted features regarding the Uniformity-Tolerance tradeoff to assess their quality, and measure the similarity of representations using linear Center Kernel Alignement. These quantitative measurements reinforce our observations from the performance benchmarks and show substantial differences between the learned representations of all types of CNNs despite being optimized with the same approach.

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