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Learning A Cascaded Non-Local Residual Network for Super-resolving Blurry Images

Learning A Cascaded Non-Local Residual Network for Super-res...

引用

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

作者： Bai, Haoran Cheng, Songsheng Tang, Jinhui Pan, Jinshan Nanjing Univ Sci & Technol Nanjing Peoples R China

ISBN: (纸本)9781665448994

Deblurring low-resolution images is quite challenging as blur exists in the images and the resolution of the images is low. Existing deblurring methods usually require high-resolution input while the super-resolution methods usually assume that the blur is known or small. Simply applying the deblurring and super-resolution does not solve this problem well. In this paper, we develop an effective cascaded non-local residual network which cascades the deblurring module and super-resolution module to estimate latent high-resolution images from blurry low-resolution ones. The network first uses the deblurring module to generate intermediate clear features and then develops a non-local residual network (NLRN) as the super-resolution module to generate clear high-resolution images from the intermediate clear features. To better constrain the network and reduce the training difficulty, we develop an effective constraint based on image gradients for edge preservation and adopt the progressive upsampling mechanism. We train the proposed network in an end-to-end manner. Both quantitative and qualitative results on the benchmarks demonstrate the effectiveness of the proposed method. Moreover, the proposed method achieves top-3 performance on the low-resolution track of the NTIRE 2021 Image Deblurring Challenge.

关键词： Training computer vision Image edge detection conferences Superresolution Benchmark testing Image restoration

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Ternary Feature Masks: zero-forgetting for task-incremental learning

Ternary Feature Masks: zero-forgetting for task-incremental ...

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

作者： Masana, Marc Tuytelaars, Tinne Van de Weijer, Joost Comp Vis Ctr Barcelona Spain Katholieke Univ Leuven ESAT PSI Leuven Belgium

ISBN: (纸本)9781665448994

We propose an approach without any forgetting to continual learning for the task-aware regime, where at inference the task-label is known. By using ternary masks we can upgrade a model to new tasks, reusing knowledge from previous tasks while not forgetting anything about them. Using masks prevents both catastrophic forgetting and backward transfer. We argue - and show experimentally - that avoiding the former largely compensates for the lack of the latter, which is rarely observed in practice. In contrast to earlier works, our masks are applied to the features (activations) of each layer instead of the weights. This considerably reduces the number of mask parameters for each new task;with more than three orders of magnitude for most networks. The encoding of the ternary masks into two bits per feature creates very little overhead to the network, avoiding scalability issues. To allow already learned features to adapt to the current task without changing the behavior of these features for previous tasks, we introduce task-specific feature normalization. Extensive experiments on several finegrained datasets and ImageNet show that our method outperforms current state-of-the-art while reducing memory overhead in comparison to weight-based approaches.

关键词： computer vision Scalability conferences Encoding pattern recognition Computational efficiency Task analysis

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Weighted Multi-Kernel Prediction Network for Burst Image Super-Resolution

Weighted Multi-Kernel Prediction Network for Burst Image Sup...

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

作者： Cho, Wooyeong Son, Sanghyeok Kim, Dae-Shik Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (纸本)9781665448994

Burst image super-resolution is an ill-posed problem tha' aims to restore a high-resolution (HR) image from a sequence of low-resolution (LR) burst images. To restore a photo-realistic HR image using their abundant information, it is essential to align each burst of frames containing random hand-held motion. Some kernel prediction networks (KPNs) that are operated without external motion compensation such as optical flow estimation have been applied to burst image processing as implicit image alignment modules. However, the existing methods do not consider the interdependencies among the kernels of different sizes that have a significant effect on each pixel. In this paper, we propose a novel weighted multi-kernel prediction network (WMKPN) that can learn the discriminative features on each pixel for burst image super-resolution. Our experimental results demonstrate that WMKPN improves the visual quality of super-resolved images. To the best of our knowledge, it outperforms the state-of-the-art within kernel prediction methods and multiple frame super-resolution (MFSR) on both the Zurich RAW to RGB and BurstSR datasets.

关键词： Visualization Computational modeling Superresolution Prediction methods Motion compensation Image restoration pattern recognition

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GAN-Based Data Augmentation and Anonymization for Skin-Lesion Analysis: A Critical Review

GAN-Based Data Augmentation and Anonymization for Skin-Lesio...

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

作者： Bissoto, Alceu Valle, Eduardo Avila, Sandra Univ Campinas UNICAMP Inst Comp IC Campinas Brazil Univ Campinas UNICAMP RECOD Lab Sch Elect & Comp Engn FEEC Campinas Brazil

ISBN: (纸本)9781665448994

Despite the growing availability of high-quality public datasets, the lack of training samples is still one of the main challenges of deep-learning for skin lesion analysis. Generative Adversarial Networks (GANs) appear as an enticing alternative to alleviate the issue, by synthesizing samples indistinguishable from real images, with a plethora of works employing them for medical applications. Nevertheless, carefully designed experiments for skin-lesion diagnosis with GAN-based data augmentation show favorable results only on out-of-distribution test sets. For GAN-based data anonymization - where the synthetic images replace the real ones - favorable results also only appear for out-of-distribution test sets. Because of the costs and risks associated with GAN usage, those results suggest caution in their adoption for medical applications.

关键词： Training Biomedical equipment computer vision conferences Medical services Generative adversarial networks Skin

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CASSOD-Net: Cascaded and Separable Structures of Dilated Convolution for Embedded vision Systems and Applications

CASSOD-Net: Cascaded and Separable Structures of Dilated Con...

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

作者： Chen, Tse-Wei Wang, Deyu Tao, Wei Wen, Dongchao Yin, Lingxiao Ito, Tadayuki Osa, Kinya Kato, Masami Canon Inc Device Technol Dev Headquarters Tokyo Japan Canon Innovat Solut Beijing Co Ltd Beijing Peoples R China

ISBN: (纸本)9781665448994

The field of view (FOV) of convolutional neural networks is highly related to the accuracy of inference. Dilated convolutions are known as an effective solution to the problems which require large FOVs. However, for general-purpose hardware or dedicated hardware, it usually takes extra time to handle dilated convolutions compared with standard convolutions. In this paper, we propose a network module, Cascaded and Separable Structure of Dilated (CASSOD) Convolution, and a special hardware system to handle the CASSOD networks efficiently. A CASSOD-Net includes multiple cascaded 2 x 2 dilated filters, which can be used to replace the traditional 3x3 dilated filters without decreasing the accuracy of inference. Two example applications, face detection and image segmentation, are tested with dilated convolutions and the proposed CASSOD modules. The new network for face detection achieves higher accuracy than the previous work with only 47% of filter weights in the dilated convolution layers of the context module. Moreover, the proposed hardware system can accelerate the computations of dilated convolutions, and it is 2.78 times faster than traditional hardware systems when the filter size is 3 x 3.

关键词： Image segmentation Machine vision conferences Hardware pattern recognition Face detection Convolutional neural networks

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Connecting Language and vision for Natural Language-Based Vehicle Retrieval

Connecting Language and Vision for Natural Language-Based Ve...

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

作者： Bai, Shuai Zheng, Zhedong Wang, Xiaohan Lin, Junyang Zhang, Zhu Zhou, Chang Yang, Hongxia Yang, Yi Alibaba Grp DAMO Acad Hangzhou Peoples R China Univ Technol Sydney ReLER Lab Sydney NSW Australia Zhejiang Univ Hangzhou Peoples R China

ISBN: (纸本)9781665448994

Vehicle search is one basic task for the efficient traffic management in terms of the AI City. Most existing practices focus on the image-based vehicle matching, including vehicle re-identification and vehicle tracking. In this paper, we apply one new modality, i.e., the language description, to search the vehicle of interest and explore the potential of this task in the real-world scenario. The natural language-based vehicle search poses one new challenge of fine-grained understanding of both vision and language modalities. To connect language and vision, we propose to jointly train the state-of-the-art vision models with the transformer-based language model in an end-to-end manner. Except for the network structure design and the training strategy, several optimization objectives are also revisited in this work. The qualitative and quantitative experiments verify the effectiveness of the proposed method. Our proposed method has achieved the 1st place on the 5th AI City Challenge, yielding competitive performance 18.69% MRR accuracy on the private test set. We hope this work can pave the way for the future study on using language description effectively and efficiently for real-world vehicle retrieval systems. The code will be available at https://***/ShuaiBai623/AIC2021-T5-CLV.

关键词： Training Smart cities computer architecture Search problems Robustness pattern recognition Task analysis

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A Review and Efficient Implementation of Scene Graph Generation Metrics

A Review and Efficient Implementation of Scene Graph Generat...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Julian Lorenz Robin Schön Katja Ludwig Rainer Lienhart University of Augsburg Germany

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Scene graph generation has emerged as a prominent research field in computer vision, witnessing significant advancements in the recent years. However, despite these strides, precise and thorough definitions for the metrics used to evaluate scene graph generation models are lacking. In this paper, we address this gap in the literature by providing a review and precise definition of commonly used metrics in scene graph generation. Our comprehensive examination clarifies the underlying principles of these metrics and can serve as a reference or introduction to scene graph ***, to facilitate the usage of these metrics, we introduce a standalone Python package called SGBench that efficiently implements all defined metrics, ensuring their accessibility to the research community. Additionally, we present a scene graph benchmarking web service, that enables researchers to compare scene graph generation methods and increase visibility of new methods in a central *** of our code can be found under https://***/sgbench/.

关键词： Measurement computer vision Solid modeling Reviews Web services conferences Computational modeling

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NTIRE 2021 Multi-modal Aerial View Object Classification Challenge

NTIRE 2021 Multi-modal Aerial View Object Classification Cha...

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

作者： Liu, Jerrick Inkawhich, Nathan Nina, Oliver Timofte, Radu Duan, Yuru Li, Gongzhe Geng, Xueli Cai, Huanqia Air Force Res Lab Albuquerque NM 87117 USA Univ Illinois Urbana IL 61801 USA Duke Univ Durham NC 27706 USA Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Northwestern Polytech Univ Changan Campus Xian Shaanxi Peoples R China Beihang Univ Beijing Peoples R China Xidian Univ Key Lab Intelligent Percept & Image Understanding Xian Peoples R China Dengzhuang South Rd Beijing Peoples R China

ISBN: (纸本)9781665448994

In this paper, we introduce the first Challenge on Multi-modal Aerial View Object Classification (MAVOC) in conjunction with the NTIRE 2021 workshop at CVPR. This challenge is composed of two different tracks using EO and SAR imagery. Both EO and SAR sensors possess different advantages and drawbacks. The purpose of this competition is to analyze how to use both sets of sensory information in complementary ways. We discuss the top methods submitted for this competition and evaluate their results on our blind test set. Our challenge results show significant improvement of more than 15% accuracy from our current baselines for each track of the competition.

关键词： Image sensors conferences Radar tracking Radar polarimetry Sensors pattern recognition Task analysis

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Efficient Pre-trained Features and Recurrent Pseudo-Labeling in Unsupervised Domain Adaptation

Efficient Pre-trained Features and Recurrent Pseudo-Labeling...

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

作者： Zhang, Youshan Davison, Brian D. Lehigh Univ Comp Sci & Engn Bethlehem PA 18015 USA

ISBN: (纸本)9781665448994

Domain adaptation (DA) mitigates the domain shift problem when transferring knowledge from one annotated domain to another similar but different unlabeled domain. However, existing models often utilize one of the ImageNet models as the backbone without exploring others, and fine-tuning or retraining the backbone ImageNet model is also time-consuming. Moreover, pseudo-labeling has been used to improve the performance in the target domain, while how to generate confident pseudo labels and explicitly align domain distributions has not been well addressed. In this paper, we show how to efficiently opt for the best pre-trained features from seventeen well-known ImageNet models in unsupervised DA problems. In addition, we propose a recurrent pseudo-labeling model using the best pre-trained features (termed PRPL) to improve classification performance. To show the effectiveness of PRPL, we evaluate it on three benchmark datasets, Office+Caltech-10, Office31, and Office-Home. Extensive experiments show that our model reduces computation time and boosts the mean accuracy to 98.1%, 92.4%, and 81.2%, respectively, substantially outperforming the state of the art.

关键词： Training Adaptation models computer vision Computational modeling conferences Optimized production technology Benchmark testing

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Supervised Contrastive Replay: Revisiting the Nearest Class Mean Classifier in Online Class-Incremental Continual Learning

Supervised Contrastive Replay: Revisiting the Nearest Class ...

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

作者： Mai, Zheda Li, Ruiwen Kim, Hyunwoo Sanner, Scott Univ Toronto Toronto ON Canada LG AI Res Seoul South Korea

ISBN: (纸本)9781665448994

Online class-incremental continual learning (CL) studies the problem of learning new classes continually from an online non-stationary data stream, intending to adapt to new data while mitigating catastrophic forgetting. While memory replay has shown promising results, the recency bias in online learning caused by the commonly used Softmax classifier remains an unsolved challenge. Although the Nearest-Class-Mean (NCM) classifier is significantly undervalued in the CL community, we demonstrate that it is a simple yet effective substitute for the Softmax classifier. It addresses the recency bias and avoids structural changes in the fully-connected layer for new classes. Moreover, we observe considerable and consistent performance gains when replacing the Softmax classifier with the NCM classifier for several state-of-the-art replay methods. To leverage the NCM classifier more effectively, data embeddings belonging to the same class should be clustered and well-separated from those with a different class label. To this end, we contribute Supervised Contrastive Replay (SCR), which explicitly encourages samples from the same class to cluster tightly in embedding space while pushing those of different classes further apart during replay-based training. Overall, we observe that our proposed SCR substantially reduces catastrophic forgetting and outperforms state-of-the-art CL methods by a significant margin on a variety of datasets.

关键词： Training computer vision conferences Performance gain pattern recognition

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