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SwinFuSR: an image fusion-inspired model for RGB-guided thermal image super-resolution

SwinFuSR: an image fusion-inspired model for RGB-guided ther...

引用

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

作者： Cyprien Arnold Philippe Jouvet Lama Seoud Polytechnique Montréal Montréal Canada CHU Sainte Justine Montréal Canada

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Thermal imaging plays a crucial role in various applications, but the inherent low resolution of commonly available infrared (IR) cameras limits its effectiveness. Conventional super-resolution (SR) methods often struggle with thermal images due to their lack of high-frequency details. Guided SR leverages information from a high-resolution image, typically in the visible spectrum, to enhance the reconstruction of a high-res IR image from the low-res input. Inspired by SwinFusion, we propose SwinFuSR, a guided SR architecture based on Swin transformers. In real world scenarios, however, the guiding modality (e.g. RBG image) may be missing, so we propose a training method that improves the robustness of the model in this case. Our method has few parameters and outperforms state of the art models in terms of Peak Signal to Noise Ratio (PSNR) and Structural SIMilarity (SSIM). In Track 2 of the PBVS 2024 Thermal Image Super-Resolution Challenge, it achieves 3rd place in the PSNR metric. Our code and pretained weights are available at https://***/visionICLab/SwinFuSR.

关键词： Training Measurement PSNR conferences Superresolution computer architecture Transformers

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Domain Adaptation Using Pseudo Labels for COVID-19 Detection

Domain Adaptation Using Pseudo Labels for COVID-19 Detection

引用

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

作者： Runtian Yuan Qingqiu Li Junlin Hou Jilan Xu Yuejie Zhang Rui Feng Hao Chen School of Computer Science Fudan University China School of Academy for Engineering and Technology Fudan University China Department of Computer Science and Engineering The Hong Kong University of Science and Technology China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Deep learning has offered advanced analytical capabilities to enhance the accuracy and efficiency of detecting COVID-19 through complex pattern recognition in medical imaging data. However, the variability across datasets from different domains poses a significant challenge to the generalization abilities of deep learning models. In this paper, we propose a novel two-stage framework for domain adaptation of COVID-19 detection. Initially, We train a model on annotated data from both domains, integrating contrastive representation learning and a modified version of CORAL loss to minimize domain discrepancies. In the subsequent stage, we employ a pseudo-labeling strategy to effectively utilize non-annotated data from the target domain, further enhancing the model’s adaptability and performance. The effectiveness of our approach is demonstrated through extensive experiments, showing significant improvements in COVID-19 detection performance compared to the baseline model. On the COVID-19 domain adaptation leaderboard in the 4th COV19D Competition, our approach ranked 1st with a Macro F1 Score of 77.55%.

关键词： COVID-19 Representation learning Deep learning Training Adaptation models computer vision conferences

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Region-based Appearance and Flow Characteristics for Anomaly Detection in Infrared Surveillance Imagery

Region-based Appearance and Flow Characteristics for Anomaly...

引用

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

作者： Yona Falinie A. Gaus Neelanjan Bhowmik Brian K. S. Isaac-Medina Hubert P. H. Shum Amir Atapour-Abarghouei Toby P. Breckon Department of Computer Science Durham University Durham UK Department of Engineering Durham University Durham UK

Anomaly detection is a classical problem within automated visual surveillance, namely the determination of the normal from the abnormal when operational data availability is highly biased towards one class (normal) due to both insufficient sample size, and inadequate distribution coverage for the other class (abnormal). In this work, we propose the dual use of both visual appearance and localized motion characteristics, derived from optic flow, applied on a per-region basis to facilitate object-wise anomaly detection within this context. Leveraging established object localization techniques from a region proposal network, optic flow is extracted from each object region and combined with appearance in the far infrared (thermal) band to give a 3-channel spatiotemporal tensor representation for each object (1 × thermal - spatial appearance; 2 × optic flow magnitude as x and y components - temporal motion). This formulation is used as the basis for training contemporary semi-supervised anomaly detection approaches in a region-based manner such that anomalous objects can be detected as a combination of appearance and/or motion within the scene. Evaluation is performed using the Long-Term infrared (thermal) Imaging (LTD) benchmark dataset against which successful detection of both anomalous object appearance and motion characteristics are demonstrated using a range of semi-supervised anomaly detection approaches.

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Investigating Calibration and Corruption Robustness of Post-hoc Pruned Perception CNNs: An Image Classification Benchmark Study

Investigating Calibration and Corruption Robustness of Post-...

引用

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

作者： Pallavi Mitra Gesina Schwalbe Nadja Klein Continental AG Germany University of Lübeck Germany TU Dortmund Germany

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance in many computer vision tasks. However, high computational and storage demands hinder their deployment into resource-constrained environments, such as embedded devices. Model pruning helps to meet these restrictions by reducing the model size, while maintaining superior performance. Meanwhile, safety-critical applications pose more than just resource and performance constraints. In particular, predictions must not be overly confident, i.e., provide properly calibrated uncertainty estimations (proper uncertainty calibration), and CNNs must be robust against corruptions like naturally occurring input perturbations (natural corruption robustness). This work investigates the important trade-off between uncertainty calibration, natural corruption robustness, and performance for current state-of-research post-hoc CNN pruning techniques in the context of image classification tasks. Our study reveals that post-hoc pruning substantially improves the model’s uncertainty calibration, performance, and natural corruption robustness, sparking hope for safe and robust embedded CNNs. Furthermore, uncertainty calibration and natural corruption robustness are not mutually exclusive targets under pruning, as evidenced by the improved safety aspects obtained by post-hoc unstructured pruning with increasing compression.

关键词： Performance evaluation computer vision Uncertainty Image coding Computational modeling Robustness Calibration

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Disentangling Local and Global Information for Light Field Depth Estimation

Disentangling Local and Global Information for Light Field D...

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

作者： Xueting Yang Junli Deng Rongshan Chen Ruixuan Cong Wei Ke Hao Sheng School of Information and Communication Engineering Communication University of China Beijing P.R. China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing P.R. China Beihang Hangzhou Innovation Institute Yuhang Xixi Octagon City Hangzhou P.R. China Faculty of Applied Sciences Macao Polytechnic University Macao SAR P.R. China

Accurate depth estimation from light field images is essential for various applications. Deep learning-based techniques have shown great potential in addressing this problem while still face challenges such as sensitivity to occlusions and difficulties in handling untextured areas. To overcome these limitations, we propose a novel approach that utilizes both local and global features in the cost volume for depth estimation. Specifically, our hybrid cost volume network consists of two complementary sub-modules: a 2D ContextNet for global context information and a matching cost volume for local feature information. We also introduce an occlusion-aware loss that accounts for occlusion areas to improve depth estimation quality. We demonstrate the effectiveness of our approach on the UrbanLF and HCInew datasets, showing significant improvements over existing methods, especially in occluded and untextured regions. Our method disentangles local feature and global semantic information explicitly, reducing the occlusion and untextured area reconstruction error and improving the accuracy of depth estimation.

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Tracked-Vehicle Retrieval by Natural Language Descriptions with Multi-Contextual Adaptive Knowledge

Tracked-Vehicle Retrieval by Natural Language Descriptions w...

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

作者： Huy Dinh-Anh Le Quang Qui-Vinh Nguyen Duc Trung Luu Truc Thi-Thanh Chau Nhat Minh Chung Synh Viet-Uyen Ha School of Computer Science and Engineering International University Ho Chi Minh City Vietnam Vietnam National University Ho Chi Minh City Vietnam

This paper introduces our solution for Track 2 in AI City Challenge 2023. The task is tracked-vehicle retrieval by natural language descriptions with a real-world dataset of various scenarios and cameras. Our solution mainly focuses on four points: (1) To address the linguistic ambiguity in the language query, we leverage our proposed standardized version for text descriptions for the domain-adaptive training and post-processing stage. (2) Our baseline vehicle retrieval model utilizes CLIP to extract robust visual and textual feature representations to learn the unified cross-modal representations between textual and visual features. (3) Our proposed semi-supervised domain adaptive (SSDA) training method is leveraged to address the domain gap between the train and test set. (4) Finally, we propose a multi-contextual post-processing technique that prunes out the wrong results based on multi-contextual attributes information that effectively boosts the final retrieval results. Our proposed framework has yielded a competitive performance of 82.63% MRR accuracy on the test set, achieving 1st place in the competition. Codes will be available at https://***/zef1611/AIC23_NLRetrieval_HCMIU_CVIP

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

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

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

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

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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...

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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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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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