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Cross-dataset Learning for Generalizable Land Use Scene Classification

Cross-dataset Learning for Generalizable Land Use Scene Clas...

引用

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

作者： Gominski, Dimitri Gouet-Brunet, Valerie Chen, Liming Univ Copenhagen Geog Copenhagen Denmark IGN LaSTIG St Mande France Ecole Cent Lyon LIRIS Ecully France

ISBN: (纸本)9781665487399

Few-shot and cross-domain land use scene classification methods propose solutions to classify unseen classes or unseen visual distributions, but are hardly applicable to realworld situations due to restrictive assumptions. Few-shot methods involve episodic training on restrictive training subsets with small feature extractors, while cross-domain methods are only applied to common classes. The underlying challenge remains open: can we accurately classify new scenes on new datasets? In this paper, we propose a new framework for few-shot, cross-domain classification. Our retrieval-inspired approach(1) exploits the interrelations in both the training and testing data to output class labels using compact descriptors. Results show that our method can accurately produce land-use predictions on unseen datasets and unseen classes, going beyond the traditional few-shot or cross-domain formulation, and allowing cross-dataset training.

关键词： Training Visualization computer vision Image analysis conferences Image retrieval Feature extraction

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CNLL: A Semi-supervised Approach For Continual Noisy Label Learning

CNLL: A Semi-supervised Approach For Continual Noisy Label L...

引用

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

作者： Karim, Nazmul Khalid, Umar Esmaeili, Ashkan Rahnavard, Nazanin Univ Cent Florida Dept Elect & Comp Engn Orlando FL 32816 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The task of continual learning requires careful design of algorithms that can tackle catastrophic forgetting. However, the noisy label, which is inevitable in a real-world scenario, seems to exacerbate the situation. While very few studies have addressed the issue of continual learning under noisy labels, long training time and complicated training schemes limit their applications in most cases. In contrast, we propose a simple purification technique to effectively cleanse the online data stream that is both cost-effective and more accurate. After purification, we perform fine-tuning in a semi-supervised fashion that ensures the participation of all available samples. Training in this fashion helps us learn a better representation that results in state-of-the-art (SOTA) performance. Through extensive experimentation on 3 benchmark datasets, MNIST, CIFAR10 and CIFAR100, we show the effectiveness of our proposed approach. We achieve a 24.8% performance gain for CIFAR10 with 20% noise over previous SOTA methods. Our code is publicly available.(1)

关键词： Training computer vision Codes Purification conferences Benchmark testing Performance gain

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A robust non-blind deblurring method using deep denoiser prior

A robust non-blind deblurring method using deep denoiser pri...

引用

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

作者： Fang, Yingying Zhang, Hao Wong, Hok Shing Zeng, Tieyong Imperial Coll London London England Chinese Univ Hong Kong Shatin Hong Kong Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The existing non-blind deblurring methods are mostly susceptible to noise in the given blurring kernel, which is usually estimated from the observed image. This will produce undesirable ringing artifacts around the recovered edges when the given kernel is not accurate enough. Besides, the noise and outliers in the observed images may also severely degrade the performance of the deblurring methods. Considering these factors, we designed a robust non-blind deblurring method taking all these noises into account. In this paper, we propose a kernel error term to rectify the given kernel in the midst of the deconvolution process. A residual error term is also introduced to deal with the outliers caused by noise or saturation. A deep learning denoiser prior is adopted to reserve the fine textures in the recovered image. The experiments show clearly that the proposed method achieves remarkable progress in both the visual quality and the numerical results of the recovered images compared to the state-of-the-art deblurring methods.

关键词： Deep learning Measurement Visualization Deconvolution Image edge detection conferences pattern recognition

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Nonuniformly Dehaze Network for Visible Remote Sensing Images

Nonuniformly Dehaze Network for Visible Remote Sensing Image...

引用

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

作者： Chen, Zhaojie Li, Qi Feng, Huajun Xu, Zhihai Chen, Yueting Zhejiang Univ Coll Opt Sci & Engn Hangzhou Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Nonuniform haze on remote sensing images degrades image quality and hinders many high-level tasks. In this paper, we propose a Nonuniformly Dehaze Network towards nonuniform haze on visible remote sensing images. To extract robust haze-aware features, we propose Nonuniformly Excite (NE) module. Inspired by the well-known gather-excite attention module, NE module works in a map-excite manner. In the map operation, we utilize a proposed Dual Attention Dehaze block to extract local enhanced features. In the gather operation, we utilize a strided deformable convolution to nonuniformly process features and extract nonlocal haze-aware features. In the excite operation, we employ a pixel-wise attention between local enhanced features and nonlocal haze-aware features, to gain finer haze-aware features. Moreover, we recursively embed NE modules in a multi-scale framework. It helps not only significantly reduce network's parameters, but also recursively deliver and fuse haze-aware features from higher levels, which makes learning more efficient. Experiments demonstrate that the proposed network performs favorably against the state-of-the-art methods on both synthetic and real-world images.

关键词： Image quality computer vision Fuses Convolution conferences Feature extraction pattern recognition

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Update Compression for Deep Neural Networks on the Edge

Update Compression for Deep Neural Networks on the Edge

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

作者： Chen, Bo Bakhshi, Ali Batista, Gustavo Ng, Brian Chin, Tat-Jun Univ Adelaide Adelaide SA Australia Univ New South Wales Sydney NSW Australia

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

An increasing number of artificial intelligence (AI) applications involve the execution of deep neural networks (DNNs) on edge devices. Many practical reasons motivate the need to update the DNN model on the edge device post-deployment, such as refining the model, concept drift, or outright change in the learning task. In this paper, we consider the scenario where retraining can be done on the server side based on a copy of the DNN model, with only the necessary data transmitted to the edge to update the deployed model. However, due to bandwidth constraints, we want to minimise the transmission required to achieve the update. We develop a simple approach based on matrix factorisation to compress the model update-this differs from compressing the model itself. The key idea is to preserve existing knowledge in the current model and optimise only small additional parameters for the update which can be used to reconstitute the model on the edge. We compared our method to similar techniques used in federated learning;our method usually requires less than half of the update size of existing methods to achieve the same accuracy.

关键词： Deep learning Neural networks Refining Redundancy Bandwidth Data models pattern recognition

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Semantic Segmentation for Thermal Images: A Comparative Survey

Semantic Segmentation for Thermal Images: A Comparative Surv...

引用

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

作者： Kutuk, Zulfiye Algan, Gorkem Aselsan Inc Dept Image Proc & Comp Vis Technol Ankara Turkey

ISBN: (纸本)9781665487399

Semantic segmentation is a challenging task since it requires excessively more low-level spatial information of the image compared to other computer vision problems. The accuracy of pixel-level classification can be affected by many factors, such as imaging limitations and the ambiguity of object boundaries in an image. Conventional methods exploit three-channel RGB images captured in the visible spectrum with deep neural networks (DNN). Thermal images can significantly contribute during the segmentation since thermal imaging cameras are capable of capturing details despite the weather and illumination conditions. Using infrared spectrum in semantic segmentation has many real-world use cases, such as autonomous driving, medical imaging, agriculture, defense industry, etc. Due to this wide range of use cases, designing accurate semantic segmentation algorithms with the help of infrared spectrum is an important challenge. One approach is to use both visible and infrared spectrum images as inputs. These methods can accomplish higher accuracy due to enriched input information, with the cost of extra effort for the alignment and processing of multiple inputs. Another approach is to use only thermal images, enabling less hardware cost for smaller use cases. Even though there are multiple surveys on semantic segmentation methods, the literature lacks a comprehensive survey centered explicitly around semantic segmentation using infrared spectrum. This work aims to fill this gap by presenting algorithms in the literature and categorizing them by their input images.

关键词： Image segmentation computer vision Costs Semantics Neural networks Lighting Robustness

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Adversarial Machine Learning Attacks Against Video Anomaly Detection Systems

Adversarial Machine Learning Attacks Against Video Anomaly D...

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

作者： Mumcu, Furkan Doshi, Keval Yilmaz, Yasin Univ S Florida 4202 E Fowler Ave Tampa FL 33620 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Anomaly detection in videos is an important computer vision problem with various applications including automated video surveillance. Although adversarial attacks on image understanding models have been heavily investigated, there is not much work on adversarial machine learning targeting video understanding models and no previous work which focuses on video anomaly detection. To this end, we investigate an adversarial machine learning attack against video anomaly detection systems, that can be implemented via an easy-to-perform cyber-attack. Since surveillance cameras are usually connected to the server running the anomaly detection model through a wireless network, they are prone to cyber-attacks targeting the wireless connection. We demonstrate how Wi-Fi deauthentication attack, a notoriously easy-to-perform and effective denial-of-service (DoS) attack, can be utilized to generate adversarial data for video anomaly detection systems. Specifically, we apply several effects caused by the Wi-Fi deauthentication attack on video quality (e.g., slow down, freeze, fast forward, low resolution) to the popular benchmark datasets for video anomaly detection. Our experiments with several state-of-the-art anomaly detection models show that the attackers can significantly undermine the reliability of video anomaly detection systems by causing frequent false alarms and hiding physical anomalies from the surveillance system.

关键词： computer vision Computational modeling Wireless networks Video surveillance Adversarial machine learning Synchronization Servers

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Continual Learning with Transformers for Image Classification

Continual Learning with Transformers for Image Classificatio...

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

作者： Ermis, Beyza Zappella, Giovanni Wistuba, Martin Rawal, Aditya Archambeau, Cedric AWS Berlin Germany AWS Santa Clara CA USA

ISBN: (纸本)9781665487399

In many real-world scenarios, data to train machine learning models become available over time. However, neural network models struggle to continually learn new concepts without forgetting what has been learnt in the past. This phenomenon is known as catastrophic forgetting and it is often difficult to prevent due to practical constraints, such as the amount of data that can be stored or the limited computation sources that can be used. Moreover, training large neural networks, such as Transformers, from scratch is very costly and requires a vast amount of training data, which might not be available in the application domain of interest. A recent trend indicates that dynamic architectures based on an expansion of the parameters can reduce catastrophic forgetting efficiently in continual learning, but this needs complex tuning to balance the growing number of parameters and barely share any information across tasks. As a result, they struggle to scale to a large number of tasks without significant overhead. In this paper, we validate in the computer vision domain a recent solution called Adaptive Distillation of Adapters (ADA), which is developed to perform continual learning using pre-trained Transformers and Adapters on text classification tasks. We empirically demonstrate on different classification tasks that this method maintains a good predictive performance without retraining the model or increasing the number of model parameters over the time. Besides it is significantly faster at inference time compared to the state-of-the-art methods.

关键词： Training computer vision Adaptation models Computational modeling Neural networks Training data Predictive models

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Transformaly - Two (Feature Spaces) Are Better Than One

Transformaly - Two (Feature Spaces) Are Better Than One

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

作者： Cohen, Matan Jacob Avidan, Shai Tel Aviv Univ Blavatnik Sch Comp Sci Tel Aviv Israel Tel Aviv Univ Sch Elect Engn Tel Aviv Israel

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Anomaly detection is a well-established research area that seeks to identify samples outside of a predetermined distribution. An anomaly detection pipeline is comprised of two main stages: (1) feature extraction and (2) normality score assignment. Recent papers used pre-trained networks for feature extraction achieving state-of-the-art results. However, the use of pre-trained networks does not fully-utilize the normal samples that are available at train time. This paper suggests taking advantage of this information by using teacher-student training. In our setting, a pretrained teacher network is used to train a student network on the normal training samples. Since the student network is trained only on normal samples, it is expected to deviate from the teacher network in abnormal cases. This difference can serve as a complementary representation to the pre-trained feature vector. Our method - Transformaly - exploits a pre-trained vision Transformer (ViT) to extract both feature vectors: the pre-trained (agnostic) features and the teacher-student (fine-tuned) features. We report state-of-the-art AUROC results in both the common unimodal setting, where one class is considered normal and the rest are considered abnormal, and the multimodal setting, where all classes but one are considered normal, and just one class is considered abnormal(1).

关键词： Training Visualization computer vision conferences Pipelines computer architecture Feature extraction

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Self-supervised Learning for Sonar Image Classification

Self-supervised Learning for Sonar Image Classification

引用

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

作者： Preciado-Grijalva, Alan Wehbe, Bilal Firvida, Miguel Bande Valdenegro-Toro, Matias German Res Ctr Artificial Intelligence D-28359 Bremen Germany Bonn Rhein Sieg Univ Appl Sci D-53757 St Augustin Germany Univ Groningen Dept AI NL-9747 AG Groningen Netherlands

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Self-supervised learning has proved to be a powerful approach to learn image representations without the need of large labeled datasets. For underwater robotics, it is of great interest to design computer vision algorithms to improve perception capabilities such as sonar image classification. Due to the confidential nature of sonar imaging and the difficulty to interpret sonar images, it is challenging to create public large labeled sonar datasets to train supervised learning algorithms. In this work, we investigate the potential of three self-supervised learning methods (RotNet, Denoising Autoencoders, and Jigsaw) to learn high-quality sonar image representation without the need of human labels. We present pre-training and transfer learning results on real-life sonar image datasets. Our results indicate that self-supervised pre-training yields classification performance comparable to supervised pre-training in a few-shot transfer learning setup across all three methods. Code and self-supervised pre-trained models are be available at agrija9/ssl-sonar-images.

关键词： computer vision Transfer learning Supervised learning Noise reduction Sonar Self-supervised learning Image representation

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