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A Temporal Encoder-Decoder Approach to Extracting Blood Volume Pulse Signal Morphology from Face Videos

A Temporal Encoder-Decoder Approach to Extracting Blood Volu...

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2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Li, Fulan Thapa, Surendrabikram Bhat, Shreyas Sarkar, Abhijit Abbott, A. Lynn Virginia Tech Bradley Department of Electrical and Computer Engineering United States Virginia Tech Department of Computer Science United States Virginia Tech Transportation Institute United States

ISBN: (纸本)9798350302493

This paper considers methods for extracting blood volume pulse (BVP) representations from video of the human face. Whereas most previous systems have been concerned with estimating vital signs such as average heart rate, this paper addresses the more difficult problem of recovering BVP signal morphology. We present a new approach that is inspired by temporal encoder-decoder architectures that have been used for audio signal separation. As input, this system accepts a temporal sequence of RGB (red, green, blue) values that have been spatially averaged over a small portion of the face. The output of the system is a temporal sequence that approximates a BVP signal. In order to reduce noise in the recovered signal, a separate processing step extracts individual pulses and performs normalization and outlier removal. After these steps, individual pulse shapes have been extracted that are sufficiently distinct to support biometric authentication. Our findings demonstrate the effectiveness of our approach in extracting BVP signal morphology from facial videos, which presents exciting opportunities for further research in this area. The source code is available at https://***/Adleof/CVPM-2023-Temporal-Encoder-Decoder-iPPG. © 2023 ieee.

关键词： Morphology

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Learning Multi-scale Representations with Single-stream Network for Video Retrieval

Learning Multi-scale Representations with Single-stream Netw...

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2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Wang, Chia-Hui Tseng, Yu-Chee Chiang, Ting-Hui Chen, Yan-Ann National Yang Ming Chiao Tung University Department of Computer Science Taiwan National Yang Ming Chiao Tung University College of AI Taiwan Chunghwa Telecom Laboratories Advanced Technology Laboratory Taoyuan Taiwan Yuan Ze University Department of Computer Science and Engineering Taiwan

ISBN: (纸本)9798350302493

With the explosive growth of video contents in the Internet, video retrieval has become an important issue that can benefit video recommendation and copyright detection. Since the key features of a video may distribute in distant regions of a lengthy video, several works have made a success by exploiting multi-stream, multi-scale architectures to learn and merge distant features. However, a multi-stream network is costly in terms of memory and computing overhead. The number of scales and these scales are handcrafted and fixed once a model is finalized. Further, being more complicated, multi-stream networks are more prone to being overfitting and lead to poorer generalization. This paper proposes a single-stream network with built-in dilated spatial and temporal learning capability. By combining with modern techniques, including Denoising Autoencoder, Squeeze-and-Excitation Attention, and Triplet Comparative Mechanism, our model achieves state-of-the-art performance in several video retrieval tasks on the FIVR200K, CC WEB VIDEO, and EVVE datasets. © 2023 ieee.

关键词： Excited states

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Transfer Learning from Synthetic In-vitro Soybean Pods Dataset for In-situ Segmentation of On-branch Soybean Pods

Transfer Learning from Synthetic In-vitro Soybean Pods Datas...

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

作者： Yang, Si Zheng, Lihua Chen, Xieyuanli Zabawa, Laura Zhang, Man Wang, Minjuan China Agr Univ Coll Informat & Elect Engn Beijing Peoples R China China Agr Univ Minist Educ Key Lab Smart Agr Syst Beijing Peoples R China Univ Bonn Inst Geodesy & Geoinformat Bonn Germany

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The mature soybean plants are of complex architecture with pods frequently touching each other, posing a challenge for in-situ segmentation of on-branch soybean pods. Deep learning-based methods can achieve accurate training and strong generalization capabilities, but it demands massive labeled data, which is often a limitation, especially for agricultural applications. As lacking the labeled data to train an in-situ segmentation model for on-branch soybean pods, we propose a transfer learning from synthetic in-vitro soybean pods. First, we present a novel automated image generation method to rapidly generate a synthetic in-vitro soybean pods dataset with plenty of annotated samples. The in-vitro soybean pods samples are overlapped to simulate the frequently physically touching of on-branch soybean pods. Then, we design a two-step transfer learning. In the first step, we finetune an instance segmentation network pretrained by a source domain (MS COCO dataset) with a synthetic target domain (in-vitro soybean pods dataset). In the second step, transferring from simulation to reality is performed by finetuning on a few real-world mature soybean plant samples. The experimental results show the effectiveness of the proposed two-step transfer learning method, such that AP50 was 0.80 for the real-world mature soybean plant test dataset, which is higher than that of direct adaptation and its AP50 was 0.77. Furthermore, the visualizations of in-situ segmentation results of on-branch soybean pods show that our method performs better than other methods, especially when soybean pods overlap densely.

关键词： Deep learning Transfer learning computer vision Instance segmentation Plant phenotyping

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Sports Field Registration via Keypoints-aware Label Condition

Sports Field Registration via Keypoints-aware Label Conditio...

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

作者： Chu, Yen-Jui Su, Jheng-Wei Hsiao, Kai-Wen Lien, Chi-Yu Fan, Shu-Ho Hu, Min-Chun Lee, Ruen-Rone Yao, Chih-Yuan Chu, Hung-Kuo Natl Tsing Hua Univ Hsinchu Taiwan Delta Res Ctr Hsinchu Taiwan Natl Taiwan Univ Sci & Technol Taipei Taiwan

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We propose a novel deep learning framework for sports field registration. The typical algorithmic flow for sports field registration involves extracting field-specific features (e.g., corners, lines, etc.) from field image and estimating the homography matrix between a 2D field template and the field image using the extracted features. Unlike previous methods that strive to extract sparse field features from field images with uniform appearance, we tackle the problem differently. First, we use a grid of uniformly distributed keypoints as our field-specific features to increase the likelihood of having sufficient field features under various camera poses. Then we formulate the keypoints detection problem as an instance segmentation with dynamic filter learning. In our model, the convolution filters are generated dynamically, conditioned on the field image and associated keypoint identity, thus improving the robustness of prediction results. To extensively evaluate our method, we introduce a new soccer dataset, called TS-WorldCup, with detailed field markings on 3812 time-sequence images from 43 videos of SoccerWorld Cup 2014 and 2018. The experimental results demonstrate that our method outperforms state-of-the-arts on the TS-WorldCup dataset in both quantitative and qualitative evaluations. Both the code and dataset are available online(1).

关键词： Image segmentation Predictive models Feature extraction Robustness pattern recognition Sparse matrices Task analysis

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CoVIO: Online Continual Learning for Visual-Inertial Odometry

CoVIO: Online Continual Learning for Visual-Inertial Odometr...

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2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Vödisch, Niclas Cattaneo, Daniele Burgard, Wolfram Valada, Abhinav University of Freiburg Germany University of Technology Nuremberg Germany

ISBN: (纸本)9798350302493

Visual odometry is a fundamental task for many applications on mobile devices and robotic platforms. Since such applications are oftentimes not limited to predefined target domains and learning-based vision systems are known to generalize poorly to unseen environments, methods for continual adaptation during inference time are of significant interest. In this work, we introduce CoVIO for online continual learning of visual-inertial odometry. CoVIO effectively adapts to new domains while mitigating catastrophic forgetting by exploiting experience replay. In particular, we propose a novel sampling strategy to maximize image diversity in a fixed-size replay buffer that targets the limited storage capacity of embedded devices. We further provide an asynchronous version that decouples the odometry estimation from the network weight update step enabling continuous inference in real time. We extensively evaluate CoVIO on various real-world datasets demonstrating that it successfully adapts to new domains while outperforming previous methods. The code of our work is publicly available at http://***. © 2023 ieee.

关键词： computer vision

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MAPLE-Edge: A Runtime Latency Predictor for Edge Devices

MAPLE-Edge: A Runtime Latency Predictor for Edge Devices

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

作者： Nair, Saeejith Abbasi, Saad Wong, Alexander Shafiee, Mohammad Javad Univ Waterloo Waterloo ON Canada DarwinAI Waterloo ON Canada

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Neural Architecture Search (NAS) has enabled automatic discovery of more efficient neural network architectures, especially for mobile and embedded vision applications. Although recent research has proposed ways of quickly estimating latency on unseen hardware devices with just a few samples, little focus has been given to the challenges of estimating latency on runtimes using optimized graphs, such as TensorRT and specifically for edge devices. As devices like NVIDIA's Jetsons get more popular in embedded computing and robotics, we observe a pressing need to more accurately estimate inference latency of neural network architectures on diverse runtimes, including highly optimized ones. In this work, we propose MAPLE-Edge, an edge device-oriented extension of MAPLE, the state-of-the-art latency predictor for general purpose hardware, where we train a regression network on architecture-latency pairs in conjunction with a hardware-runtime descriptor to effectively estimate latency on a diverse pool of edge devices. Compared to MAPLE, MAPLE-Edge can describe the runtime and target device platform using a much smaller set of CPU performance counters that are widely available on all Linux kernels, while still achieving up to +49.6% accuracy gains against previous state-of-the-art baseline methods on optimized edge device runtimes, using just 10 measurements from an unseen target device. We also demonstrate that unlike MAPLE which performs best when trained on a pool of devices sharing a common runtime, MAPLE-Edge can effectively generalize across runtimes by applying a trick of normalizing performance counters by the operator latency, in the measured hardware-runtime descriptor. Lastly, we show that for runtimes exhibiting lower than desired accuracy, performance can be boosted by collecting additional samples from the target device, with an extra 90 samples translating to gains of nearly +40%.

关键词： Performance evaluation Training Runtime Linux Neural networks computer architecture Pressing

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SwiniPASSR: Swin Transformer based Parallax Attention Network for Stereo Image Super-Resolution

SwiniPASSR: Swin Transformer based Parallax Attention Networ...

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

作者： Jin, Kai Wei, Zeqiang Yang, Angulia Guo, Sha Gao, Mingzhi Zhou, Xiuzhuang Guo, Guodong Bigo Technol Pte Ltd Beijing Peoples R China Beijing Univ Posts & Telecommun Smart Med Innovat Lab Beijing Peoples R China Peking Univ Inst Digital Media Beijing Peoples R China Baidu Res Inst Deep Learning Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

With binocular cameras being widely accepted, the study of stereo image super resolution (Stereo SR) has received increasing attention. Different from single image super resolution (SISR) setting, it is more challenging for utilizing both intra-view and cross-view information. Although prior convolution-based works have achieved admirable progress, few attempts have explored the possibility of the Transformer-based architecture for stereo image SR, which has demonstrated promising performance in several visual tasks. In this paper, we propose a novel approach namely SwiniPASSR, which adopts Swin Transformer as the backbone, meanwhile incorporating it with the Bi-directional Parallax Attention Module (biPAM) to maximize auxiliary information given by the binocular mechanism. Even Transformer and parallax attention mechanism (PAM) have been separately proved usefulness by prior studies, we find that simply integrating convolution-based PAM with Transformer or directly optimizing for stereo SR problem was may not achieve desirable result. We therefore introduced a conversion layer to resolve integration and adopted progressive training strategy to learn disparity correspondence through progressively enlarged receptive fields. Both extensive experiments and ablation studies demonstrate the effectiveness of our proposed SwiniPASSR. In particular, in the NTIRE 2022: Stereo Image Super-Resolution Challenge, we report 23.71dB PSNR and 0.7295 SSIM performance which ranked 2nd place on the leaderboard.

关键词： Training Convolutional codes Visualization Superresolution Estimation Transformers pattern recognition

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Masked Jigsaw Puzzle: A Versatile Position Embedding for vision Transformers

Masked Jigsaw Puzzle: A Versatile Position Embedding for Vis...

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

作者： Ren, Bin Liu, Yahui Song, Yue Bi, Wei Cucchiara, Rita Sebe, Nicu Wang, Wei Univ Pisa Pisa Italy Univ Trento Trento TN Italy Tencent AI Lab Shenzhen Peoples R China Beijing Jiaotong Univ Beijing Peoples R China Univ Modena & Reggio Emilia Modena Italy

ISBN: (纸本)9798350301298

Position Embeddings (PEs), an arguably indispensable component in vision Transformers (ViTs), have been shown to improve the performance of ViTs on many vision tasks. However, PEs have a potentially high risk of privacy leakage since the spatial information of the input patches is exposed. This caveat naturally raises a series of interesting questions about the impact of PEs on accuracy, privacy, prediction consistency, etc. To tackle these issues, we propose a Masked Jigsaw Puzzle (MJP) position embedding method. In particular, MJP first shuffles the selected patches via our block-wise random jigsaw puzzle shuffle algorithm, and their corresponding PEs are occluded. Meanwhile, for the non-occluded patches, the PEs remain the original ones but their spatial relation is strengthened via our dense absolute localization regressor. The experimental results reveal that 1) PEs explicitly encode the 2D spatial relationship and lead to severe privacy leakage problems under gradient inversion attack;2) Training ViTs with the naively shuffled patches can alleviate the problem, but it harms the accuracy;3) Under a certain shuffle ratio, the proposed MJP not only boosts the performance and robustness on large-scale datasets (i.e., ImageNet-1K and ImageNet-C, -A/O) but also improves the privacy preservation ability under typical gradient attacks by a large margin. The source code and trained models are available at https://***/yhlleo/ MJP.

关键词： Deep learning architectures and techniques

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Proceedings - 2020 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2020

Proceedings - 2020 IEEE/CVF Conference on Computer Vision an...

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2020 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2020

ISBN: (纸本)9781728193601

The proceedings contain 523 papers. The topics discussed include: latent fingerprint image enhancement based on progressive generative adversarial network;zero-shot learning in the presence of hierarchically coarsened labels;multivariate confidence calibration for object detection;context-guided super-class inference for zero-shot detection;learning sparse ternary neural networks with entropy-constrained trained ternarization (EC2T);now that i can see, i can improve: enabling data-driven finetuning of CNNs on the edge;enhancing facial data diversity with style-based face aging;a simplified framework for zero-shot cross-modal sketch data retrieval;unsupervised single image super-resolution network (USISResNet) for real-world data using generative adversarial network;cross-regional oil palm tree detection;and leaf spot attention network for apple leaf disease identification.

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WSRD: A Novel Benchmark for High Resolution Image Shadow Removal

WSRD: A Novel Benchmark for High Resolution Image Shadow Rem...

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2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Vasluianu, Florin-Alexandru Seizinger, Tim Timofte, Radu University of Würzburg Computer Vision Laboratory Caidas & Ifi Germany

ISBN: (纸本)9798350302493

Shadow removal is an important computer vision task, whose aim is to successfully detect the shadow affected area appearing through light occlussion, followed by a photorealistic restoration of the affected image contents, textures, and details. After decades of research, a multitude of hand-crafted restoration techniques were proposed, following different observations on shadow formation models, with scenes altered in particular conditions. However, the increased popularity of deep learning based solutions enabled a significant step forward for the shadow removal solutions, both in terms of reconstruction fidelity and perceptual properties. However, the publicly available datasets remain focused around a particularly low complexity setup, with a low variety of light occluders and affected backgrounds, and with limited representation for more complex light interactions and complex shadow patterns. In this work, we propose WSRD, a novel benchmark for high resolution image shadow removal, characterized by a large variety in terms or represented objects, backgrounds and light occluders. We study more complex interactions, combining self shadows with externally casted shadows, to further extend the study of the phenomenon, its factors and effects. To prove WSRD as a relevant benchmark, we propose DNSR, a novel shadow removal method, comparing the results on WSRD with the performance level observed on other well-established benchmarks like ISTD and ISTD+. We validate our approach comparing with existing state-of-the-art (SOTA) methods, improving both in reconstruction fidelity and perceptual properties, setting a new SOTA for the field. © 2023 ieee.

关键词： Restoration

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