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Detecting Out-Of-Distribution Earth Observation Images with Diffusion Models

Detecting Out-Of-Distribution Earth Observation Images with ...

引用

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

作者： Georges Le Bellier Nicolas Audebert Cnam CEDRIC EA4629 Paris France Univ. Gustave Eiffel ENSG IGN LASTIG Saint-Mandé France

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Earth Observation imagery can capture rare and unusual events, such as disasters and major landscape changes, whose visual appearance contrasts with the usual observations. Deep models trained on common remote sensing data will output drastically different features for these out-of-distribution samples, compared to those closer to their training dataset. Detecting them could therefore help anticipate changes in the observations, either geographical or environmental. In this work, we show that the reconstruction error of diffusion models can effectively serve as unsupervised out-of-distribution detectors for remote sensing images., using as a plausibility score. Moreover, we introduce ODEED, a novel reconstruction-based scorer using the probability-flow ODE of diffusion models. We validate it experimentally on SpaceNet 8 with various scenarios, such as classical OOD detection with geographical shift and near-OOD setups: pre/post-flood and non-flooded/flooded image recognition. We show that our ODEED scorer significantly outperforms other diffusion-based and discriminative baselines on the more challenging near-OOD scenarios of flood image detection, where OOD images are close to the distribution tail. We aim to pave the way towards better use of generative models for anomaly detection in remote sensing.

关键词： Training Visualization Tail Diffusion models Data models Satellite images pattern recognition

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Cross-view Aggregation Network For Stereo Image Super-Resolution

Cross-view Aggregation Network For Stereo Image Super-Resolu...

引用

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

作者： Zhitao Chen Tao Lu Kanghui Zhao Bolin Zhu Zhen Li Jiaming Wang Yanduo Zhang Hubei Key Laboratory of Intelligent Robot Wuhan Institute of Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Although stereo image super-resolution has been extensively studied, many existing works only rely on attention in a single epipolar direction to reconstruct stereo images. In the case of asymmetric parallax images, these methods often struggle to capture reliable stereo correspondence, resulting in reconstructed images suffering from blurring and artifacts. In this paper, we propose a novel method called Cross-View Aggregation Network for Stereo Image Super-Resolution (CANSSR) and explore the relationship between multi-directional epipolar lines to construct reliable stereo correspondence. Specifically, we propose a multidirectional cross-view aggregation module (MCAM) that effectively captures multi-directional stereo correspondence and obtains cross-view complementary information. Furthermore, we design a channel-spatial aggregation module (CSAM) that aggregates multi-order global-local information in intra-view to reconstruct clearer texture features. In addition, we equip a large kernel convolution in the Feedforward Network to acquire richer detailed texture information. The extensive experiments conclusively demonstrate that CANSSR outperforms the state-of-the-art method both qualitatively and quantitatively in terms of stereo image super-resolution on the Flickr 1024 and Middlebury datasets.

关键词： Image databases Convolution computer network reliability Superresolution pattern recognition Reliability Web sites

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Distribution-Aware Multi-Label FixMatch for Semi-Supervised Learning on CheXpert

Distribution-Aware Multi-Label FixMatch for Semi-Supervised ...

引用

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

作者： Sontje Ihler Felix Kuhnke Timo Kuhlgatz Thomas Seel Institute of Mechatronic Systems Leibniz Universität Hannover

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Semi-supervised learning (SSL) has achieved remarkable success for multiclass classification in recent years, yielding a promising solution for medical image classification where labeled data is scarce but unlabeled images are accessible. In the context of multi-label problems however, SSL is still under-explored. In this work we adapt Fix-Match to the multi-label scenario, specifically focusing on CheXpert, a multi-label chest X-ray classification dataset which is imbalanced and only partially labeled. Leveraging distribution alignment, our proposed method, ML-FixMatch+DA, achieves solid performance gains in SSL tasks (AUC: +2.6%) and in a missing label scenario (AUC: +1.9%). In contrast to previous work we achieve a performance gain on CheXpert using FixMatch. We show that in contrast to multiclass FixMatch, where distribution alignment is optional, it is essential for multi-label FixMatch to handle class imbalance and generate reliable (positive and negative) pseudo-labels. Our pseudo-label selection is based on a single threshold for all classes and handles imbalance with no prior knowledge on label distributions. Our adaptation keeps the simplicity of the original multi-class FixMatch with no added hyperparameters (even for imbalanced data) and demonstrates the feasibility of simple SSL for multi-label problems, filling a crucial gap in the literature.

关键词： Semisupervised learning Performance gain Solids Filling Robustness pattern recognition Mirrors

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XoFTR: Cross-modal Feature Matching Transformer

XoFTR: Cross-modal Feature Matching Transformer

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

作者： Önder Tuzcuoğlu Aybora Köksal Buğra Sofu Sinan Kalkan A. Aydın Alatan Dept. of Electrical and Electronics Eng. Center for Image Analysis Middle East Technical University Ankara Turkey ROKETSAN Inc. Ankara Turkey Dept. of Computer Eng. Center for Image Analysis Middle East Technical University Ankara Turkey

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We introduce, XoFTR, a cross-modal cross-view method for local feature matching between thermal infrared (TIR) and visible images. Unlike visible images, TIR images are less susceptible to adverse lighting and weather conditions but present difficulties in matching due to significant texture and intensity differences. Current hand-crafted and learning-based methods for visible-TIR matching fall short in handling viewpoint, scale, and texture diversities. To address this, XoFTR incorporates masked image modeling pre-training and fine-tuning with pseudo-thermal image augmentation to handle the modality differences. Additionally, we introduce a refined matching pipeline that adjusts for scale discrepancies and enhances match reliability through sub-pixel level refinement. To validate our approach, we collect a comprehensive visible-thermal dataset, and show that our method outperforms existing methods on many benchmarks. Code and dataset at https://***/OnderT/XoFTR.

关键词： Learning systems Image matching Pipelines Lighting Benchmark testing Transformers Image augmentation

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On Moving Object Segmentation from Monocular Video with Transformers

On Moving Object Segmentation from Monocular Video with Tran...

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International conference on computer vision workshops (ICCV workshops)

作者： Christian Homeyer Christoph Schnörr Robert Bosch GmbH Corporate Research Computer Vision Lab Hildesheim Germany Image and Pattern Analysis Group Heidelberg University Germany Image and Pattern Analysis Group Heidelberg University Germany

Moving object detection and segmentation from a single moving camera is a challenging task, requiring an understanding of recognition, motion and 3D geometry. Combining both recognition and reconstruction boils down to a fusion problem, where appearance and motion features need to be combined for classification and *** this paper, we present a novel fusion architecture for monocular motion segmentation - M 3 Former, which leverages the strong performance of transformers for segmentation and multi-modal fusion. As reconstructing motion from monocular video is ill-posed, we systematically analyze different 2D and 3D motion representations for this problem and their importance for segmentation performance. Finally, we analyze the effect of training data and show that diverse datasets are required to achieve SotA performance on Kitti and Davis. Code will be released upon publication.

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Fast and Accurate Quantized Camera Scene Detection on Smartphones, Mobile AI 2021 Challenge: Report

Fast and Accurate Quantized Camera Scene Detection on Smartp...

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

作者： Ignatov, Andrey Malivenko, Grigory Timofte, Radu Chen, Sheng Xia, Xin Liu, Zhaoyan Zhang, Yuwei Zhu, Feng Li, Jiashi Xiao, Xuefeng Tian, Yuan Wu, Xinglong Kyrkou, Christos Chen, Yixin Zhang, Zexin Peng, Yunbo Lin, Yue Dutta, Saikat Das, Sourya Dipta Shah, Nisarg A. Kumar, Himanshu Ge, Chao Wu, Pei-Lin Du, Jin-Hua Batutin, Andrew Federico, Juan Pablo Lyda, Konrad Khojoyan, Levon Thanki, Abhishek Paul, Sayak Siddiqui, Shahid Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland AI Witchlabs Lausanne Switzerland ByteDance Inc Beijing Peoples R China Univ Cyprus KIOS Res & Innovat Ctr Excellence Nicosia Cyprus Netease Games AI Lab Beijing Peoples R China Indian Inst Technol Madras Chennai Tamil Nadu India Jadavpur Univ Kolkata India Indian Inst Technol Jodhpur Karwar India Chinese Acad Sci Inst Automat Nanjing Artificial Intelligence Chip Res Beijing Peoples R China DataArt Inc New York NY USA PyImageSearch Mumbai Maharashtra India Univ Cyprus KIOS Ctr Excellence Nicosia Cyprus

ISBN: (纸本)9781665448994

Camera scene detection is among the most popular computer vision problem on smartphones. While many custom solutions were developed for this task by phone vendors, none of the designed models were available publicly up until now. To address this problem, we introduce the first Mobile AI challenge, where the target is to develop quantized deep learning-based camera scene classification solutions that can demonstrate a real-time performance on smartphones and IoT platforms. For this, the participants were provided with a large-scale CamSDD dataset consisting of more than 11K images belonging to the 30 most important scene categories. The runtime of all models was evaluated on the popular Apple Bionic A11 platform that can be found in many iOS devices. The proposed solutions are fully compatible with all major mobile AI accelerators and can demonstrate more than 100-200 FPS on the majority of recent smartphone platforms while achieving a top-3 accuracy of more than 98%. A detailed description of all models developed in the challenge is provided in this paper.

关键词： computer vision Runtime Image analysis Computational modeling conferences Cameras Real-time systems

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Fast-NTK: Parameter-Efficient Unlearning for Large-Scale Models

Fast-NTK: Parameter-Efficient Unlearning for Large-Scale Mod...

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

作者： Guihong LiM Hsiang Hsu Chun-Fu Richard Chen Radu Marculescu The University of Texas at Austin USA Global Technology Applied Research JPMorgan Chase USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The rapid growth of machine learning has spurred legislative initiatives such as "the Right to be Forgotten," allowing users to request data removal. In response, machine unlearning proposes the selective removal of unwanted data without the need for retraining from scratch. While the Neural-Tangent-Kernel (NTK) based unlearning method excels in performance, it suffers from significant computational complexity, especially for large-scale models and datasets. To improve this situation, our work introduces "Fast-NTK," a novel NTK-based unlearning algorithm that significantly reduces the computational complexity by incorporating parameter-efficient fine-tuning methods, such as fine-tuning batch normalization layers in a CNN or visual prompts in a vision transformer. Our experimental results demonstrate scalability to really large neural networks and datasets (e.g., 88M parameters and 5k images), surpassing the limitations of previous full-model NTK-based approaches designed for smaller cases (e.g., 8M parameters and 500 images). Notably, our approach maintains a performance comparable to the traditional methods of retraining on the retain set alone. Fast-NTK can thus enable practical and scalable NTK-based unlearning in deep neural networks.

关键词： computer vision Visualization Computational modeling Scalability conferences Artificial neural networks Machine learning

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Dr-SAM: An End-to-End Framework for Vascular Segmentation, Diameter Estimation, and Anomaly Detection on Angiography Images.

Dr-SAM: An End-to-End Framework for Vascular Segmentation, D...

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

作者： Vazgen Zohranyan Vagner Navasardyan Hayk Navasardyan Jan Borggrefe Shant Navasardyan Yerevan State University (YSU) ServiceTitan Inc. Ruhr-Universität Bochum (RUB) Johannes Wesling University Hospital Department of Radiology Neuroradiology and Nuclear Medicine Synopsys Armenia CJSC Picsart AI Research (PAIR)

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Recent advancements in AI have significantly transformed medical imaging, particularly in angiography, by enhancing diagnostic precision and patient care. However existing works are limited in analyzing the aorta and iliac arteries, above all for vascular anomaly detection and characterization. To close this gap, we propose Dr-SAM, a comprehensive multi-stage framework for vessel segmentation, diameter estimation, and anomaly analysis aiming to examine the peripheral vessels through angiography images. For segmentation we introduce a customized positive/negative point selection mechanism applied on top of the Segment Anything Model (SAM), specifically for medical (Angiography) images. Then we propose a morphological approach to determine the vessel diameters followed by our histogram-driven anomaly detection approach. Moreover, we introduce a new benchmark dataset for the comprehensive analysis of peripheral vessel angiography images which we hope can boost the upcoming research in this direction leading to enhanced diagnostic precision and ultimately better health outcomes for individuals facing vascular issues.

关键词： Image segmentation Angiography conferences Estimation Benchmark testing pattern recognition Anomaly detection

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Monitoring Social Insect Activity with Minimal Human Supervision

Monitoring Social Insect Activity with Minimal Human Supervi...

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

作者： Tarun Sharma Julian M. Wagner Sara Beery William B. Dickson Michael H. Dickinson Joseph Parker California Institute of Technology USA MIT USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Tracking the behavior of animals and their group dynamics in nature offers a crucial look into the delicate ecological networks that compose wildlife diversity. The velvety tree ant (Liometopum occidentale) is an ecologically dominant ant species found in South Western North America; their extensive foraging activity shapes forest communities, and their nests are a biodiversity hot-spot for a multitude of symbiotic invertebrates (myrmecophiles). Despite their vital role in the ecosystem, their activity is largely unstudied. In this work, we develop a multi-sensor camera trap, named ’Ethocam,’ to capture ant behavioral patterns in the field, and combine this technology with a computer vision approach to track colony activity in an undisturbed fashion. We demonstrate an accurate system for counting ants built with minimal human labeling. We show that L. occidentale activity drops rapidly through the morning and study the effect of environmental conditions on ant count. We also report the occurrences of the ants’ interactions with other invertebrates in our camera trap data. Together, these findings demonstrate the potential of our system to capture the behavior of Liometopum occidentale as well as its complex associations with various local species including symbionts, potentially at landscape scale. Our study provides proof of concept for the promise of low-cost remote monitoring of social insect populations.

关键词： Symbiosis computer vision Shape Insects Wildlife Vegetation Cameras

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PitcherNet: Powering the Moneyball Evolution in Baseball Video Analytics

PitcherNet: Powering the Moneyball Evolution in Baseball Vid...

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

作者： Jerrin Bright Bavesh Balaji Yuhao Chen David A Clausi John S Zelek University of Waterloo Waterloo Canada

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In the high-stakes world of baseball, every nuance of a pitcher’s mechanics holds the key to maximizing performance and minimizing runs. Traditional analysis methods often rely on pre-recorded offline numerical data, hindering their application in the dynamic environment of live games. Broadcast video analysis, while seemingly ideal, faces significant challenges due to factors like motion blur and low resolution. To address these challenges, we introduce PitcherNet, an end-to-end automated system that analyzes pitcher kinematics directly from live broadcast video, thereby extracting valuable pitch statistics including velocity, release point, pitch position, and release extension. This system leverages three key components: (1) Player tracking and identification by decoupling actions from player kinematics; (2) Distribution and depth-aware 3D human modeling; and (3) Kinematic-driven pitch statistics. Experimental validation demonstrates that PitcherNet achieves robust analysis results with 96.82% accuracy in pitcher tracklet identification, reduced joint position error by 1.8mm and superior analytics compared to baseline methods. By enabling performance-critical kinematic analysis from broadcast video, PitcherNet paves the way for the future of baseball analytics by optimizing pitching strategies, preventing injuries, and unlocking a deeper understanding of pitcher mechanics, forever transforming the game.

关键词： Solid modeling Three-dimensional displays Tracking Visual analytics Kinematics Games pattern recognition

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