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LGFN: Lightweight Light Field Image Super-Resolution using Local Convolution Modulation and Global Attention Feature Extraction

LGFN: Lightweight Light Field Image Super-Resolution using L...

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

作者： Zhongxin Yu Liang Chen Zhiyun Zeng Kunping Yang Shaofei Luo Shaorui Chen Cheng Zhong Fujian Normal University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Capturing different intensity and directions of light rays at the same scene, Light field (LF) can encode the 3D scene cues into a 4D LF image, which has a wide range of applications (i.e., post-capture refocusing and depth sensing). LF image super-resolution (SR) aims to improve the image resolution limited by the performance of LF camera sensor. Although existing methods have achieved promising results, the practical application of these models is limited because they are not lightweight enough. In this paper, we propose a lightweight model named LGFN, which integrates the local and global features of different views and the features of different channels for LF image SR. Specifically, owing to neighboring regions of the same pixel position in different sub-aperture images exhibit similar structural relationships, we design a lightweight CNN-based feature extraction module (namely, DGCE) to extract local features better through feature modulation. Meanwhile, as the position beyond the boundaries in the LF image presents a large disparity, we propose an efficient spatial attention module (namely, ESAM) which uses decomposable large-kernel convolution to obtain an enlarged receptive field and an efficient channel attention module (namely, ECAM). Compared with the existing LF image SR models with large parameter, our model has a parameter of 0.45M and a FLOPs of 19.33G, which has achieved a competitive effect. Extensive experiments with ablation studies demonstrate the effectiveness of our proposed method, which ranked the second place in the Track 2 Fidelity & Efficiency of NTIRE2024 Light Field Super Resolution Challenge and the seventh place in the Track 1 Fidelity.

关键词： computer vision Three-dimensional displays Convolution conferences Superresolution Modulation Feature extraction

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Privacy-friendly Synthetic Data for the Development of Face Morphing Attack Detectors

Privacy-friendly Synthetic Data for the Development of Face ...

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

作者： Naser Damer Cé sar Augusto Fontanillo Ló pez Meiling Fang Noé mie Spiller Minh Vu Pham Fadi Boutros Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany Centre for IT &#x0026 IP Law KU Leuven Leuven Belgium

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

The main question this work aims at answering is: "can morphing attack detection (MAD) solutions be successfully developed based on synthetic data?". Towards that, this work introduces the first synthetic-based MAD development dataset, namely the Synthetic Morphing Attack Detection Development dataset (SMDD). This dataset is utilized successfully to train three MAD backbones where it proved to lead to high MAD performance, even on completely unknown attack types. Additionally, an essential aspect of this work is the detailed legal analyses of the challenges of using and sharing real biometric data, rendering our proposed SMDD dataset extremely essential. The SMDD dataset, consisting of 30,000 attack and 50,000 bona fide samples, is publicly available for research purposes 1 .

关键词： Training Data privacy computer vision Law Face recognition conferences Training data

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EfficientNet-SAM: A Novel EffecientNet with Spatial Attention Mechanism for COVID-19 Detection in Pulmonary CT Scans

EfficientNet-SAM: A Novel EffecientNet with Spatial Attentio...

引用

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

作者： Ramy Farag Parth Upadhay Jacket Demby’s Yixiang Gao Katherin Garces Montoya Seyed Mohamad Ali Tousi Gbenga Omotara Guilherme DeSouza Department of Electrical Engineering and Computer Science Vision-Guided and Intelligent Robotics Lab - ViGIR Lab University of Missouri-Columbia

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Manual analysis and diagnosis of COVID-19 through the examination of Computed Tomography (CT) images of the lungs can be time-consuming and result in errors, especially given high volume of patients and numerous images per patient. So, we address the need for automation of this task by developing a new deep learning-based pipeline. Our motivation was sparked by the CVPR Workshop on "Domain Adaptation, Explainability and Fairness in AI for Medical Image Analysis", more specifically, the "COVID-19 Diagnosis Competition (DEF-AI-MIA COV19D)" under the same Workshop. This challenge provides an opportunity to assess our proposed pipeline for COVID-19 detection from CT scan images. The same pipeline incorporates one of the architectures in the EfficientNet "family", but with an added Spatial Attention Mechanism: EfficientNet-SAM. Also, unlike the traditional/past pipelines, which relied on a preprocessing step, our pipeline takes the raw selected input images without any such step, except for an image-selection step to simply reduce the number of CT images required for training and/or testing. Moreover, our pipeline is computationally efficient, as, for example, it does not incorporate a decoder for segmenting the lungs. It also does not combine different models nor combine RNN with a backbone, as other pipelines in the past did. Nevertheless, our pipeline outperformed all approaches presented by other teams in last year’s instance of the same challenge using the validation subset. It also placed 5th in this year’s competition, ranking less than 1.3% below the 1st place and close to 3.5% above the 6th place based on the macro-F1 score.

关键词： COVID-19 Training Visualization Attention mechanisms Computed tomography conferences Pipelines

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NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Methods and Results

NTIRE 2021 Challenge on Quality Enhancement of Compressed Vi...

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

作者： Yang, Ren Timofte, Radu Liu, Jing Xu, Yi Zhang, Xinjian Zhao, Minyi Zhou, Shuigeng Chan, Kelvin C. K. Zhou, Shangchen Xu, Xiangyu Loy, Chen Change Li, Xin Liu, Fanglong Zheng, He Jiang, Lielin Zhang, Qi He, Dongliang Li, Fu Dang, Qingqing Huang, Yibin Maggioni, Matteo Fu, Zhongqian Xiao, Shuai Li, Cheng Tanay, Thomas Song, Fenglong Chao, Wentao Guo, Qiang Liu, Yan Li, Jiang Qu, Xiaochao Hou, Dewang Yang, Jiayu Jiang, Lyn You, Di Zhang, Zhenyu Mou, Chong Koshelev, Iaroslav Ostyakov, Pavel Somov, Andrey Hao, Jia Zou, Xueyi Zhao, Shijie Sun, Xiaopeng Liao, Yiting Zhang, Yuanzhi Wang, Qing Zhan, Gen Guo, Mengxi Li, Junlin Lu, Ming Ma, Zhan Michelini, Pablo Navarrete Wang, Hai Chen, Yiyun Guo, Jingyu Zhang, Liliang Yang, Wenming Kim, Sijung Oh, Syehoon Wang, Yucong Cai, Minjie Hao, Wei Shi, Kangdi Li, Liangyan Chen, Jun Gao, Wei Liu, Wang Zhang, Xiaoyu Zhou, Linjie Lin, Sixin Wang, Ru Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Bilibili Inc Shanghai Peoples R China Fudan Univ Shanghai Peoples R China Nanyang Technol Univ S Lab Singapore Singapore Baidu Inc Dept Comp Vis Technol VIS Beijing Peoples R China Huawei Technol Co Ltd Huawei Noahs Ark Lab Shenzhen Peoples R China Meitu Inc MTLab Beijing Peoples R China Peking Univ Shenzhen Peoples R China Tencent Shenzhen Peoples R China Skolkovo Inst Sci & Technol Moscow Russia Huawei Noahs Ark Lab Shenzhen Peoples R China HiSilicon Shanghai Technol CO LTD Shanghai Peoples R China ByteDance Ltd Shenzhen Peoples R China Nanjing Univ Sch Elect Sci & Engn Nanjing Peoples R China BOE Technol Grp Co Ltd Beijing Peoples R China Tsinghua Univ Shenzhen Peoples R China SZ Da Jiang Innovat Sci & Technol Co Ltd Shenzhen Peoples R China Bluedot Seoul South Korea Hunan Univ Coll Comp Sci & Elect Engn Changsha Peoples R China McMaster Univ Hamilton ON Canada Peking Univ Sch Elect & Comp Engn Shenzhen Grad Sch Shenzhen Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9781665448994

This paper reviews the first NTIRE challenge on quality enhancement of compressed video, with focus on proposed solutions and results. In this challenge, the new Large-scale Diverse Video (LDV) dataset is employed. The challenge has three tracks. Tracks 1 and 2 aim at enhancing the videos compressed by HEVC at a fixed QP, while Track 3 is designed for enhancing the videos compressed by x265 at a fixed bit-rate. Besides, the quality enhancement of Tracks 1 and 3 targets at improving the fidelity (PSNR), and Track 2 targets at enhancing the perceptual quality. The three tracks totally attract 482 registrations. In the test phase, 12 teams, 8 teams and 11 teams submitted the final results of Tracks 1, 2 and 3, respectively. The proposed methods and solutions gauge the state-of-the-art of video quality enhancement. The homepage of the challenge: https://***/RenYang-home/NTIRE21_VEnh

关键词： computer vision Target tracking conferences Quality assessment pattern recognition Video recording

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Adaptive Bitrate Quantization Scheme Without Codebook for Learned Image Compression

Adaptive Bitrate Quantization Scheme Without Codebook for Le...

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

作者： Jonas Lö hdefink Jonas Sitzmann Andreas Bä r Tim Fingscheidt Technische Universit&#x00E4 t Braunschweig Germany

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

We propose a generic approach to quantization without codebook in learned image compression called one-hot max (OHM, Ω) quantization. It reorganizes the feature space resulting in an additional dimension, along which vector quantization yields one-hot vectors by comparing activations. Furthermore, we show how to integrate Ω quantization into a compression system with bitrate adaptation, i.e., full control over bitrate during inference. We perform experiments on both MNIST and Kodak and report on rate-distortion trade-offs comparing with the integer rounding reference. For low bitrates (< 0.4 bpp), our proposed quantizer yields better performance while exhibiting also other advantageous training and inference properties. Code is available at https://***/ifnspaml/OHMQ.

关键词： Training computer vision Image coding Codes Vector quantization conferences Bit rate

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Residual Feature Pyramid Network for Enhancement of Vascular patterns

Residual Feature Pyramid Network for Enhancement of Vascular...

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

作者： Ketan Kotwal Sé bastien Marcel Idiap Research Institute Switzerland University of Lausanne Switzerland

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

The accuracy of finger vein recognition systems gets degraded due to low and uneven contrast between veins and surroundings, often resulting in poor detection of vein patterns. We propose a finger-vein enhancement technique, ResFPN (Residual Feature Pyramid Network), as a generic preprocessing method agnostic to the recognition pipeline. A bottom-up pyramidal architecture using the novel Structure Detection block (SDBlock) facilitates extraction of veins of varied widths. Using a feature aggregation module (FAM), we combine these vein-structures, and train the proposed ResFPN for detection of veins across scales. With enhanced presentations, our experiments indicate a reduction upto 5% in the average recognition errors for commonly used recognition pipeline over two publicly available datasets. These improvements are persistent even in cross-dataset scenario where the dataset used to train the ResFPN is different from the one used for recognition.

关键词： Histograms Protocols Veins Pipelines Fingers computer architecture Network architecture

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Hot-started NAS for Task-specific Embedded Applications

Hot-started NAS for Task-specific Embedded Applications

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

作者： Lotte Hendrickx Wiebe Van Ranst Toon Goedemé EAVISE-PSI-ESAT KU Leuven Sint-Katelijne-Waver Belgium

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Neural architecture search (NAS) has proven its worth in discovering new neural networks. Combining the possibility to satisfy multiple objectives in one search, it is especially useful for getting the most out of embedded devices with limited resources. However, research into small and efficient neural networks precedes NAS. We investigate the influence of combining this pre-existing knowledge with NAS techniques, for which we propose to hot-start the NAS search with a human-designed optimal network. Our experiments show that doing so speeds up the NAS process significantly, but the resulting optimal model at the end is only marginally better. Since embedded devices are often used for a specific task, we also explore the impact of using a task-specific dataset in the NAS process. Our experiments demonstrate that for a constrained problem, a smaller network can be found as compared to a general problem.

关键词： Knowledge engineering computer vision conferences Neural networks Size measurement Search problems pattern recognition

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DOAD: Decoupled One Stage Action Detection Network

DOAD: Decoupled One Stage Action Detection Network

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

作者： Shuning Chang Pichao Wang Fan Wang Jiashi Feng Mike Zheng Shou Showlab National University of Singapore Alibaba Group National University of Singapore

ISBN: (数字)9798350302493

ISBN: (纸本)9798350302509

Localizing people and recognizing their actions from videos is a challenging task towards high-level video understanding. Existing methods are mostly two-stage based, with one stage for person bounding box generation and the other stage for action recognition. However, such two-stage methods are generally with low efficiency. We observe that directly unifying detection and action recognition normally suffers from (i) inferior learning due to different desired properties of context representation for detection and action recognition; (ii) optimization difficulty with insufficient training data. In this work, we present a decoupled onestage network dubbed DOAD, to mitigate above issues and improve the efficiency for spatio-temporal action detection. To achieve it, we decouple detection and action recognition into two branches. Specifically, one branch focuses on detection representation for actor detection, and the other one for action recognition. For the action branch, we design a transformer-based module (TransPC) to model pairwise relationships between people and context. Different from commonly used vector-based dot product in self-attention, it is built upon a novel matrix-based key and value for Hadamard attention to model person-context information. It not only exploits relationships between person pairs but also takes into account context and relative position information. The results on AVA and UCF101-24 datasets show that our method is competitive with two-stage state-of-theart methods with significant efficiency improvement.

关键词： computer vision conferences Training data Transformers Feature extraction pattern recognition Task analysis

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Improving Multi-Target Multi-Camera Tracking by Track Refinement and Completion

Improving Multi-Target Multi-Camera Tracking by Track Refine...

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

作者： Andreas Specker Lucas Florin Mickael Cormier Jü rgen Beyerer Karlsruhe Institute of Technology Fraunhofer IOSB Fraunhofer Center for Machine Learning

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Multi-camera tracking of vehicles on a city-wide level is a core component of modern traffic monitoring systems. For this task, single-camera tracking failures are the most common causes of errors concerning automatic multi-target multi-camera tracking systems. To address these problems, we propose several modules that aim at improving single-camera tracklets, e.g., appearance-based tracklet splitting, single-camera clustering, and track completion. After these track refinement steps, hierarchical clustering is used to associate the enhanced single-camera tracklets. During this stage, we leverage vehicle re-identification features as well as prior knowledge about the scene's topology. Last, the proposed track completion strategy is adopted for the cross-camera association task to obtain the final multi-camera tracks. Our method proves itself competitive: With it, we achieved 4th place in track 1 of the 2022 AI City Challenge.

关键词： computer vision conferences Urban areas Topology pattern recognition Task analysis Artificial intelligence

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Energy-Efficient Uncertainty-Aware Biomass Composition Prediction at the Edge

Energy-Efficient Uncertainty-Aware Biomass Composition Predi...

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

作者： Muhammad Zawish Paul Albert Flavio Esposito Steven Davy Lizy Abraham Walton Institute South East Technological University Ireland Center for Augmented Reasoning Australian Institute of Machine Learning Australia Department of Computer Science Saint Louis University USA Centre for Sustainable Digital Technologies at Technological University Dublin Ireland

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Clover fixates nitrogen from the atmosphere to the ground, making grass-clover mixtures highly desirable to reduce external nitrogen fertilization. Herbage containing clover additionally promotes higher food intake, resulting in higher milk production. Herbage probing however remains largely unused as it requires a time-intensive manual laboratory analysis. Without this information, farmers are unable to perform localized clover sowing or take targeted fertilization decisions. Deep learning algorithms have been proposed with the goal to estimate the dry biomass composition from images of the grass directly in the fields. The energy-intensive nature of deep learning however limits deployment to practical edge devices such as smartphones. This paper proposes to fill this gap by applying filter pruning to reduce the energy requirement of existing deep learning solutions. We report that although pruned networks are accurate on controlled, high-quality images of the grass, they struggle to generalize to real-world smartphone images that are blurry or taken from challenging angles. We address this challenge by training filter-pruned models using a variance attenuation loss so they can predict the uncertainty of their predictions. When the uncertainty exceeds a threshold, we re-infer using a more accurate unpruned model. This hybrid approach allows us to reduce energy-consumption while retaining a high accuracy. We evaluate our algorithm on two datasets: the GrassClover and the Irish clover using an NVIDIA Jetson Nano edge device. We find that we reduce energy reduction with respect to state-of-the-art solutions by 50% on average with only 4% accuracy loss.

关键词： Deep learning Accuracy Uncertainty Image edge detection Filtering algorithms Predictive models Prediction algorithms

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