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"BNN - BN = ?": Training Binary Neural Networks without Batch Normalization

"BNN - BN = ?": Training Binary Neural Networks without Batc...

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

作者： Chen, Tianlong Zhang, Zhenyu Ouyang, Xu Liu, Zechun Shen, Zhiqiang Wang, Zhangyang Univ Texas Austin Austin TX 78712 USA Univ Sci & Technol China Hefei Anhui Peoples R China Cornell Univ Ithaca NY 14853 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9781665448994

Batch normalization (BN) is a key facilitator and considered essential for state-of-the-art binary neural networks (BNN). However, the BN layer is costly to calculate and is typically implemented with non-binary parameters, leaving a hurdle for the efficient implementation of BNN training. It also introduces undesirable dependence between samples within each batch. Inspired by the latest advance on Batch Normalization Free (BN-Free) training [7], we extend their framework to training BNNs, and for the first time demonstrate that BNs can be completely removed from BNN training and inference regimes. By plugging in and customizing techniques including adaptive gradient clipping, scale weight standardization, and specialized bottleneck block, a BN-free BNN is capable of maintaining competitive accuracy compared to its BN-based counterpart. Extensive experiments validate the effectiveness of our proposal across diverse BNN backbones and datasets. For example, after removing BNs from the state-of-the-art ReActNets [38], it can still be trained with our proposed methodology to achieve 92.08%, 68.34%, and 68.0% accuracy on CIFAR-10, CIFAR-100, and ImageNet respectively, with marginal performance drop (0.23% similar to 0.44% on CIFAR and 1.40% on ImageNet). Codes and pre-trained models are available at: https://***/VITA-Group/BNN_NoBN.

关键词： Training computer vision conferences Neural networks Standardization Hardware pattern recognition

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MarkerPose: Robust Real-time Planar Target Tracking for Accurate Stereo Pose Estimation

MarkerPose: Robust Real-time Planar Target Tracking for Accu...

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

作者： Meza, Jhacson Romero, Lenny A. Marrugo, Andres G. Univ Tecnol Bolivar Cartagena Colombia

ISBN: (纸本)9781665448994

Despite the attention marker-less pose estimation has attracted in recent years, marker-based approaches still provide unbeatable accuracy under controlled environmental conditions. Thus, they are used in many fields such as robotics or biomedical applications but are primarily implemented through classical approaches, which require lots of heuristics and parameter tuning for reliable performance under different environments. In this work, we propose MarkerPose, a robust, real-time pose estimation system based on a planar target of three circles and a stereo vision system. MarkerPose is meant for high-accuracy pose estimation applications. Our method consists of two deep neural networks for marker point detection. A SuperPoint-like network for pixel-level accuracy keypoint localization and classification, and we introduce EllipSegNet, a lightweight ellipse segmentation network for sub-pixel-level accuracy keypoint detection. The marker's pose is estimated through stereo triangulation. The target point detection is robust to low lighting and motion blur conditions. We compared MarkerPose with a detection method based on classical computer vision techniques using a robotic arm for validation. The results show our method provides better accuracy than the classical technique. Finally, we demonstrate the suitability of MarkerPose in a 3D freehand ultrasound system, which is an application where highly accurate pose estimation is required. Code is available in Python and C++ at https://***/jhacsonmeza/MarkerPose.

关键词： Deep learning computer vision Ultrasonic imaging Target tracking Three-dimensional displays Pose estimation Lighting

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IB-DRR - Incremental Learning with Information-Back Discrete Representation Replay

IB-DRR - Incremental Learning with Information-Back Discrete...

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

作者： Jiang, Jian Cetin, Edoardo Celiktutan, Oya Kings Coll London Ctr Robot Res Dept Engn London England

ISBN: (纸本)9781665448994

Incremental learning aims to enable machine learning models to continuously acquire new knowledge given new classes, while maintaining the knowledge already learned for old classes. Saving a subset of training samples of previously seen classes in the memory and replaying them during new training phases is proven to be an efficient and effective way to fulfil this aim. It is evident that the larger number of exemplars the model inherits the better performance it can achieve. However, finding a trade-off between the model performance and the number of samples to save for each class is still an open problem for replay-based incremental learning and is increasingly desirable for real-life applications. In this paper, we approach this open problem by tapping into a two-step compression approach. The first step is a lossy compression, we propose to encode input images and save their discrete latent representations in the form of 'codes' that are learned using a hierarchical Vector Quantised Variational Autoencoder (VQ-VAE). In the second step, we further compress 'codes' losslessly by learning a hierarchical latent variable model with bits-back asymmetric numeral systems (BB-ANS). To compensate for the information lost in the first step compression, we introduce an Information Back (IB) mechanism that utilizes raw exemplars for a contrastive learning loss to regularise the training of a classifier. By maintaining all seen exemplars' representations in the format of 'codes', Discrete Representation Replay (DRR) outperforms the state-of-art method on CIFAR-100 by a margin of 4% average accuracy with a much less memory cost required for saving samples. Incorporated with IB and saving a small set of old raw exemplars as well, the average accuracy of DRR can be further improved by 2%.

关键词： Training computer vision Image coding conferences Memory management Machine learning pattern recognition

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Detecting Low-Rank Regions in Omnidirectional Images

Detecting Low-Rank Regions in Omnidirectional Images

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

作者： Kato, Zoltan Nagy, Gabor Humenberger, Martin Csurka, Gabriela Univ Szeged Inst Informat Szeged Hungary J Selye Univ Komarno Slovakia NAVER LABS Europe Meylan France

ISBN: (纸本)9781665448994

Planar low-rank regions commonly found in man-made environments, can be used to estimate a rectifying homography that provides valuable information about the camera and the 3D plane they observe. Methods to recover such a homography exist, but detection of low-rank regions is largely unsolved, especially for omnidirectional cameras where significant distortions make the problem even more challenging. In this paper we address this problem as follows. First we propose a method to generate a low-rank probability map on an omnidirectional image and use it to build a training set in a self-supervised manner to train deep models to predict low-rank likelihood maps for omnidirectional images. Second, we propose to adapt regular CNN operators to equirectangular images and to combine them seamlessly into a network where each layer preserves the properties of the equirectangular representation. Finally, on the new KITTI360 dataset, we show that the rectifying homography of detected low-rank regions in such predicted maps allows to factorize out the camera-plane pose up to certain ambiguities that can be easily overcome.

关键词： Training computer vision Adaptation models Three-dimensional displays conferences Predictive models Distortion

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Semi-synthesis: A fast way to produce effective datasets for stereo matching

Semi-synthesis: A fast way to produce effective datasets for...

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

作者： He, Ju Zhou, Enyu Sun, Liusheng Lei, Fei Liu, Chenyang Sun, Wenxiu Johns Hopkins Univ Baltimore MD 21218 USA SenseTime Res Hong Kong Peoples R China

ISBN: (纸本)9781665448994

Stereo matching is an important problem in computer vision which has drawn tremendous research attention for decades. Recent years, data-driven methods with convolutional neural networks (CNNs) are continuously pushing stereo matching to new heights. However, data-driven methods require large amount of training data, which is not an easy task for real stereo data due to the annotation difficulties of per-pixel ground-truth disparity. Though synthetic dataset is proposed to fill the gaps of large data demand, the fine-tuning on real dataset is still needed due to the domain variances between synthetic data and real data. In this paper, we found that in synthetic datasets, close-to-real-scene texture rendering is a key factor to boost up stereo matching performance, while close-to-real-scene 3D modeling is less important. We then propose semi-synthetic, an effective and fast way to synthesize large amount of data with close-to-real-scene texture to minimize the gap between synthetic data and real data. Extensive experiments demonstrate that models trained with our proposed semi-synthetic datasets achieve significantly better performance than with general synthetic datasets, especially on real data benchmarks with limited training data. With further fine-tuning on the real dataset, we also achieve SOTA performance on Middlebury and competitive results on KITTI and ETH3D datasets.

关键词： Training computer vision Three-dimensional displays Training data Benchmark testing Rendering (computer graphics) Environmental factors

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Hierarchical Spatial Pyramid Network For Cervical Precancerous Segmentation By Reconstructing Deep Segmentation Networks

Hierarchical Spatial Pyramid Network For Cervical Precancero...

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

作者： Meng, Zhu Zhao, Zhicheng Su, Fei Guo, Limei Beijing Univ Posts & Telecommun Beijing Peoples R China Beijing Key Lab Network Syst & Network Culture Beijing Peoples R China Peking Univ Hosp 3 Hlth Sci Ctr Beijing Peoples R China

ISBN: (纸本)9781665448994

Cervical cancer is one of the leading causes of cancer death in women aged 20 to 39 years, which emphasizes the importance of cervical precancerous diagnosis and treatment. Although there are many attempts on medical image processing, the research on the automatic diagnosis of cervical precancerous pathology is still scarce. In this paper, a challenging end-to-end automatic segmentation task for cervical precancerous diagnosis is focused. Specifically, considering that the diagnosis of cervical lesions relies heavily on spatial information, a hierarchical spatial pyramid network (HSP-Net) is proposed to enhance the representation ability of cervical structural features. First, a vertical hierarchical spatial pyramid (V-HSP) network is devised to aggregate the multiscale information during the feature extraction of the encoder. Second, a horizontal hierarchical spatial pyramid (H-HSP) network is designed to fuse information of multiscale receptive fields before and after cascading features from different branches. Experiments on the public dataset MTCHI demonstrate that HSP-Net achieves the state-of-the-art performance, reflecting the potential to assist doctors and patients clinically.

关键词： Pathology Image segmentation Fuses conferences Medical services Feature extraction pattern recognition

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A Tale of Two CILs: The Connections between Class Incremental Learning and Class Imbalanced Learning, and Beyond

A Tale of Two CILs: The Connections between Class Incrementa...

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

作者： He, Chen Wang, Ruiping Chen, Xilin Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc CAS Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

ISBN: (纸本)9781665448994

Catastrophic forgetting, the main challenge of Class Incremental Learning, is closely related to the classifier's bias due to imbalanced data, and most researchers resort to empirical techniques to remove the bias. Such anti-bias tricks share many ideas with the field of Class Imbalanced Learning, which encourages us to reflect on why these tricks work, and how we can design more principled solutions from a different perspective. In this paper, we comprehensively analyze the connections and seek possible collaborations between these two fields, i.e. Class Incremental Learning and Class Imbalanced Learning. Specifically, we first provide a panoramic view of recent bias correction tricks from the perspective of handling class imbalance. Then, we show that an adapted post-scaling technique which originates from Class Imbalanced Learning is on par with or even outperforms SOTA Class Incremental Learning method. Visualization via violin plots and polar charts further sheds light on how SOTA methods address the class imbalance problem from a more intuitive geometric perspective. These findings may encourage further infiltration between the two closely connected fields, but also raise concerns about whether it is correct that Class Incremental Learning degenerates into a class imbalance problem.

关键词： Learning systems computer vision conferences Collaboration pattern recognition

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Assessment of deep learning based blood pressure prediction from PPG and rPPG signals

Assessment of deep learning based blood pressure prediction ...

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

作者： Schrumpf, Fabian Frenzel, Patrick Aust, Christoph Osterhoff, Georg Fuchs, Mirco Leipzig Univ Appl Sci Leipzig Germany Univ Hosp Leipzig Leipzig Germany

ISBN: (纸本)9781665448994

Exploiting photoplethysmography signals (PPG) for non-invasive blood pressure (BP) measurement is interesting for various reasons. First, PPG can easily be measured using fingerclip sensors. Second, camera-based approaches allow to derive remote PPG (rPPG) signals similar to PPG and therefore provide the opportunity for non-invasive measurements of BP. Various methods relying on machine learning techniques have recently been published. Performances are often reported as the mean average error (MAE) on the data which is problematic. This work aims to analyze the PPG- and rPPG-based BP prediction error with respect to the underlying data distribution. First, we train established neural network (NN) architectures and derive an appropriate parameterization of input segments drawn from continuous PPG signals. Second, we apply this parameterization to a larger PPG dataset and train NNs to predict BP. The resulting prediction errors increase towards less frequent BP values. Third, we use transfer learning to train the NNs for rPPG based BP prediction. The resulting performances are similar to the PPG-only case. Finally, we apply a personalization technique and retrain our NNs with subject-specific data. This slightly reduces the prediction errors.

关键词： Deep learning computer vision conferences Transfer learning Artificial neural networks Photoplethysmography Blood pressure

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Generalized Unsupervised Clustering of Hyperspectral Images of Geological Targets in the Near Infrared

Generalized Unsupervised Clustering of Hyperspectral Images ...

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

作者： Gao, Angela F. Rasmussen, Brandon Kulits, Peter Scheller, Eva L. Greenberger, Rebecca Ehlmann, Bethany L. CALTECH Pasadena CA 91125 USA

ISBN: (纸本)9781665448994

The application of infrared hyperspectral imagery to geological problems is becoming more popular as data become more accessible and cost-effective. Clustering and classifying spectrally similar materials is often a first step in applications ranging from economic mineral exploration on Earth to planetary exploration on Mars. Semi-manual classification guided by expertly developed spectral parameters can be time consuming and biased, while supervised methods require abundant labeled data and can be difficult to generalize. Here we develop a fully unsupervised workflow for feature extraction and clustering informed by both expert spectral geologist input and quantitative metrics. Our pipeline uses a lightweight autoencoder followed by Gaussian mixture modeling to map the spectral diversity within any image. We validate the performance of our pipeline at submillimeter-scale with expert-labelled data from the Oman ophiolite drill core and evaluate performance at meters-scale with partially classified orbital data of Jezero Crater on Mars (the landing site for the Perseverance rover). We additionally examine the effects of various preprocessing techniques used in traditional analysis of hyperspectral imagery. This pipeline provides a fast and accurate clustering map of similar geological materials and consistently identifies and separates major mineral classes in both laboratory imagery and remote sensing imagery. We refer to our pipeline as "Generalized Pipeline for Spectroscopic Unsupervised clustering of Minerals (GyPSUM)."

关键词： Mars Pipelines Memory management Semisupervised learning Feature extraction Minerals pattern recognition

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Anti-Adversarially Manipulated Attributions for Weakly and Semi-Supervised Semantic Segmentation

Anti-Adversarially Manipulated Attributions for Weakly and S...

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2021 ieee/CVF conference on computer vision and pattern recognition, cvpr 2021

作者： Lee, Jungbeom Kim, Eunji Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Seoul Korea Republic of ASRI INMC ISRC Institute of Engineering Research Seoul National University Korea Republic of

ISBN: (纸本)9781665445092

Weakly supervised semantic segmentation produces a pixel-level localization from a classifier, but it is likely to restrict its focus to a small discriminative region of the target object. AdvCAM is an attribution map of an image that is manipulated to increase the classification score. This manipulation is realized in an anti-adversarial manner, which perturbs the images along pixel gradients in the opposite direction from those used in an adversarial attack. It forces regions initially considered not to be discriminative to become involved in subsequent classifications, and produces attribution maps that successively identify more regions of the target object. In addition, we introduce a new regularization procedure that inhibits the incorrect attribution of regions unrelated to the target object and limits the attributions of the regions that already have high scores. On PASCAL VOC 2012 test images, we achieve mIoUs of 68.0 and 76.9 for weakly and semi-supervised semantic segmentation respectively, which represent a new state-of-the-art. The code is available at: https://***/jbeomlee93/AdvCAM. © 2021 ieee

关键词： Semantic Segmentation

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