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Training Domain-invariant Object Detector Faster with Feature Replay and Slow Learner

Training Domain-invariant Object Detector Faster with Featur...

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

作者： Lee, Chaehyeon Seo, Junghoon Jung, Heechul Kyungpook Natl Univ Dept Artificial Intelligence Daegu South Korea SI Analyt Co Ltd Daejeon South Korea SI Analyt Daejeon South Korea

ISBN: (纸本)9781665448994

In deep learning-based object detection on remote sensing domain, nuisance factors, which affect observed variables while not affecting predictor variables, often matters because they cause domain changes. Previously, nuisance disentangled feature transformation (NDFT) was proposed to build domain-invariant feature extractor with with knowledge of nuisance factors. However, NDFT requires enormous time in a training phase, so it has been impractical. In this paper, we introduce our proposed method, A-NDFT, which is an improvement to NDFT. A-NDFT utilizes two acceleration techniques, feature replay and slow learner. Consequently, on a large-scale UAVDT benchmark, it is shown that our framework can reduce the training time of NDFT from 31 hours to 3 hours while still maintaining the performance. The code will be made publicly available online(1).

关键词： Training computer vision Vehicle detection conferences Object detection Detectors Benchmark testing

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Learning to predict crop type from heterogeneous sparse labels using meta-learning

Learning to predict crop type from heterogeneous sparse labe...

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

作者： Tseng, Gabriel Kerner, Hannah Nakalembe, Catherine Becker-Reshef, Inbal NASA Harvest College Pk MD 20742 USA Univ Maryland College Pk MD 20742 USA

ISBN: (纸本)9781665448994

There are many labelled datasets relating to land cover and crop type mapping that cover diverse geographies, agroecologies and land uses. However, these labels are often extremely sparse, particularly in low- and middle-income regions, with as few as tens of examples for certain crop types. This makes it challenging to train supervised machine learning models to detect specific crops in satellite observations of these regions. We investigate the utility of model-agnostic meta-learning (MAML) to learn from diverse global datasets and improve performance in data-sparse regions. We find that in a variety of countries (Togo, Kenya and Brazil) and across a variety of tasks (crop type mapping, crop vs. non-crop mapping), MAML improves performance compared to pretrained and random initial weights. We also investigate the utility of MAML for different target data-size regimes. We find MAML outperforms other methods for a wide range of training set sizes and positive to negative label ratios, indicating its general suitability for land use and crop type mapping.

关键词： Training Geography computer vision Satellites conferences Machine learning Agriculture

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Inaccuracy of State-Action Value Function For Non-Optimal Actions in Adversarially Trained Deep Neural Policies

Inaccuracy of State-Action Value Function For Non-Optimal Ac...

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

作者： Korkmaz, Ezgi KTH Royal Inst Technol Stockholm Sweden

ISBN: (纸本)9781665448994

The introduction of deep neural networks as function approximator for the state-action value function has led to the creation of a new research area for self-learning systems that explore policies from high dimensional input. While the success of deep neural policies has resulted in the deployment of these policies in diversified application domains, there are significant concerns regarding their robustness towards specifically crafted malicious perturbations introduced to their inputs. Several studies have focused on making deep neural policies resistant to such perturbations via training with the existence of these perturbations (i.e. adversarial training). In this paper we focus on conducting an investigation on the state-action value function learned by state-of-the-art adversarially trained deep neural policies and vanilla trained deep neural policies. We perform several experiments in the OpenAI Baselines and we show that the state-action value functions learned by vanilla trained deep neural policies have better estimates for the non-optimal actions than the state-of-the-art adversarially trained deep neural policies. We believe our study lays out intriguing properties of adversarial training and could be critical step towards obtaining robust and reliable policies.

关键词： Training Resistance Deep learning computer vision Systematics Perturbation methods conferences

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Anchor-based Plain Net for Mobile Image Super-Resolution

Anchor-based Plain Net for Mobile Image Super-Resolution

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

作者： Du, Zongcai Liu, Jie Tang, Jie Wu, Gangshan Nanjing Ubivers State Key Lab Novel Software Technol Nanjing Peoples R China

ISBN: (纸本)9781665448994

Along with the rapid development of real-world applications, higher requirements on the accuracy and efficiency of image super-resolution (SR) are brought forward. Though existing methods have achieved remarkable success, the majority of them demand plenty of computational resources and large amount of RAM, and thus they can not be well applied to mobile device. In this paper, we aim at designing efficient architecture for 8-bit quantization and deploy it on mobile device. First, we conduct an experiment about meta-node latency by decomposing lightweight SR architectures, which determines the portable operations we can utilize. Then, we dig deeper into what kind of architecture is beneficial to 8-bit quantization and propose anchor-based plain net (ABPN). Finally, we adopt quantization-aware training strategy to further boost the performance. Our model can outperform 8-bit quantized FSRCNN by nearly 2dB in terms of PSNR, while satisfying realistic needs at the same time. Code is avaliable at https://***/NJUJet/SR_Mobile_Quantization.

关键词： Training computer vision Quantization (signal) conferences Computational modeling Superresolution Random access memory

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A Watermarking-Based Framework for Protecting Deep Image Classifiers Against Adversarial Attacks

A Watermarking-Based Framework for Protecting Deep Image Cla...

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

作者： Sun, Chen Yang, En-Hui Univ Waterloo Dept Elect & Comp Engn Waterloo ON Canada

ISBN: (纸本)9781665448994

Although deep learning-based models have achieved tremendous success in image-related tasks, they are known to be vulnerable to adversarial examples-inputs with imperceptible, but subtly crafted perturbation which fool the models to produce incorrect outputs. To distinguish adversarial examples from benign images, in this paper, we propose a novel watermarking-based framework for protecting deep image classifiers against adversarial attacks. The proposed framework consists of a watermark encoder, a possible adversary, and a detector followed by a deep image classifier to be protected. Specific methods of watermarking and detection are also presented. It is shown by experiment on a subset of ImageNet validation dataset that the proposed framework along with the presented methods of watermarking and detection is effective against a wide range of advanced attacks (static and adaptive), achieving a near zero (effective) false negative rate for FGSM and PGD attacks (static and adaptive) with the guaranteed zero false positive rate. In addition, for all tested deep image classifiers (ResNet50V2, MobileNetV2, InceptionV3), the impact of watermarking on classification accuracy is insignificant with, on average, 0.63% and 0.49% degradation in top 1 and top 5 accuracy, respectively.

关键词： Degradation computer vision Perturbation methods conferences Watermarking Detectors pattern recognition

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DFM: A Performance Baseline for Deep Feature Matching

DFM: A Performance Baseline for Deep Feature Matching

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

作者： Efe, Ufuk Ince, Kutalmis Gokalp Alatan, A. Aydin Middle East Tech Univ Dept Elect & Elect Engn Ctr Image Anal OGAM Ankara Turkey

ISBN: (纸本)9781665448994

A novel image matching method is proposed that utilizes learned features extracted by an off-the-shelf deep neural network to obtain a promising performance. The proposed method uses pre-trained VGG architecture as a feature extractor and does not require any additional training specific to improve matching. Inspired by well-established concepts in the psychology area, such as the Mental Rotation paradigm, an initial warping is performed as a result of a preliminary geometric transformation estimate. These estimates are simply based on dense matching of nearest neighbors at the terminal layer of VGG network outputs of the images to be matched. After this initial alignment, the same approach is repeated again between reference and aligned images in a hierarchical manner to reach a good localization and matching performance. Our algorithm achieves 0.57 and 0.80 overall scores in terms of Mean Matching Accuracy (MMA) for 1 pixel and 2 pixels thresholds respectively on Hpatches dataset [4], which indicates a better performance than the state-of-the-art.

关键词： Deep learning Training Location awareness computer vision Image matching conferences Psychology

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Insights from the Future for Continual Learning

Insights from the Future for Continual Learning

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

作者： Douillard, Arthur Valle, Eduardo Ollion, Charles Robert, Thomas Cord, Matthieu Sorbonne Univ Paris France Heuritech Paris France Univerty Campinas Campinas Brazil CMAP Ecole Polytech Palaiseau France Valeo Ai Paris France

ISBN: (纸本)9781665448994

Continual learning aims to learn tasks sequentially, with (often severe) constraints on the storage of old learning samples, without suffering from catastrophic forgetting. In this work, we propose prescient continual learning, a novel experimental setting, to incorporate existing information about the classes, prior to any training data. Usually, each task in a traditional continual learning setting evaluates the model on present and past classes, the latter with a limited number of training samples. Our setting adds future classes, with no training samples at all. We introduce Ghost Model, a representation-learning-based model for continual learning using ideas from zero-shot learning. A generative model of the representation space in concert with a careful adjustment of the losses allows us to exploit insights from future classes to constraint the spatial arrangement of the past and current classes. Quantitative results on the AwA2 and aP&Y datasets and detailed visualizations showcase the interest of this new setting and the method we propose to address it.(1)

关键词： Training computer vision Computational modeling conferences Training data pattern recognition Task analysis

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DANICE: Domain adaptation without forgetting in neural image compression

DANICE: Domain adaptation without forgetting in neural image...

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

作者： Katakol, Sudeep Herranz, Luis Yang, Fei Mrak, Marta Univ Michigan Ann Arbor MI 48109 USA UAB Comp Vis Ctr Barcelona Spain BBC R&D London England

ISBN: (纸本)9781665448994

Neural image compression (NIC) is a new coding paradigm where coding capabilities are captured by deep models learned from data. This data-driven nature enables new potential functionalities. In this paper, we study the adaptability of codecs to custom domains of interest. We show that NIC codecs are transferable and that they can be adapted with relatively few target domain images. However, naive adaptation interferes with the solution optimized for the original source domain, resulting in forgetting the original coding capabilities in that domain, and may even break the compatibility with previously encoded bitstreams. Addressing these problems, we propose Codec Adaptation without Forgetting (CAwF), a framework that can avoid these problems by adding a small amount of custom parameters, where the source codec remains embedded and unchanged during the adaptation process. Experiments demonstrate its effectiveness and provide useful insights on the characteristics of catastrophic interference in NIC.

关键词： Video coding computer vision Image coding Codecs conferences Transfer learning Interference

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SRFlow-DA: Super-Resolution Using Normalizing Flow with Deep Convolutional Block

SRFlow-DA: Super-Resolution Using Normalizing Flow with Deep...

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

作者： Jo, Younghyun Yang, Sejong Kim, Seon Joo Yonsei Univ Seoul South Korea

ISBN: (纸本)9781665448994

Multiple high-resolution (HR) images can be generated from a single low-resolution (LR) image, as super-resolution (SR) is an underdetermined problem. Recently, the conditional normalizing flow-based model, SRFlow, shows remarkable performance by learning an exact mapping from HR image manifold to a latent space. The flow-based SR model allows sampling multiple output images from a learned SR space with a given LR image. In this work, we propose SRFlow-DA which has a more suitable architecture for the SR task based on the original SRFlow model. Specifically, our approach enlarges the receptive field by stacking more convolutional layers in the affine couplings, and so our model can get more expressive power. At the same time, we reduce the total number of model parameters for efficiency. Compared to SRFlow, our SRFlow-DA achieves better or comparable PSNR and LPIPS for x4 and x8 SR tasks, while having a reduced number of parameters. In addition, our method generates visually clear results without excessive sharpness artifacts.

关键词： Couplings Training Manifolds computer vision conferences Superresolution Stacking

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DAMSL: Domain Agnostic Meta Score-based Learning

DAMSL: Domain Agnostic Meta Score-based Learning

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

作者： Cai, John Cai, Bill Mei, Shen Sheng Princeton Univ Princeton NJ 08544 USA MIT Cambridge MA 02139 USA Pensees Pte Ltd Singapore Singapore

ISBN: (纸本)9781665448994

In this paper, we propose Domain Agnostic Meta Score-based Learning (DAMSL), a novel, versatile and highly effective solution that delivers significant out-performance over state-of-the-art methods for cross-domain few-shot learning. We identify key problems in previous meta-learning methods over-fitting to the source domain, and previous transfer-learning methods under-utilizing the structure of the support set. The core idea behind our method is that instead of directly using the scores from a fine-tuned feature encoder, we use these scores to create input coordinates for a domain agnostic metric space. A graph neural network is applied to learn an embedding and relation function over these coordinates to process all information contained in the score distribution of the support set. We test our model on both established CD-FSL benchmarks and new domains and show that our method overcomes the limitations of previous meta-learning and transfer-learning methods to deliver substantial improvements in accuracy across both smaller and larger domain shifts.

关键词： computer vision conferences Computational modeling Performance gain Benchmark testing Extraterrestrial measurements Boosting

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