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Extracurricular Learning: Knowledge Transfer Beyond Empirical Distribution

Extracurricular Learning: Knowledge Transfer Beyond Empirica...

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

作者： Pouransari, Hadi Javaheripi, Mojan Sharma, Vinay Tuzel, Oncel Apple Cupertino CA 95014 USA UCSD La Jolla CA USA

ISBN: (纸本)9781665448994

Knowledge distillation has been used to transfer knowledge learned by a sophisticated model (teacher) to a simpler model (student). This technique is widely used to compress model complexity. However, in most applications the compressed student model suffers from an accuracy gap with its teacher. We propose extracurricular learning, a novel knowledge distillation method, that bridges this gap by (1) modeling student and teacher output distributions;(2) sampling examples from an approximation to the underlying data distribution;and (3) matching student and teacher output distributions over this extended set including uncertain samples. We conduct rigorous evaluations on regression and classification tasks and show that compared to the standard knowledge distillation, extracurricular learning reduces the gap by 46 % to 68 %. This leads to major accuracy improvements compared to the empirical risk minimization-based training for various recent neural network architectures: 16% regression error reduction on the MPIIGaze dataset, +3.4% to +9.1% improvement in top-1 classification accuracy on the CIFAR100 dataset, and +2.9% top-1 improvement on the ImageNet dataset.

关键词： Training Uncertainty Neural networks Estimation Data models pattern recognition Task analysis

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Video Class Agnostic Segmentation Benchmark for Autonomous Driving

Video Class Agnostic Segmentation Benchmark for Autonomous D...

引用

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

作者： Siam, Mennatullah Kendall, Alex Jagersand, Martin Univ Alberta Edmonton AB Canada Wayve London England

ISBN: (纸本)9781665448994

Semantic segmentation approaches are typically trained on large-scale data with a closed finite set of known classes without considering unknown objects. In certain safety-critical robotics applications, especially autonomous driving, it is important to segment all objects, including those unknown at training time. We formalize the task of video class agnostic segmentation from monocular video sequences in autonomous driving to account for unknown objects. Video class agnostic segmentation can be formulated as an open-set or a motion segmentation problem. We discuss both formulations and provide datasets and benchmark different baseline approaches for both tracks. In the motion-segmentation track we benchmark real-time joint panoptic and motion instance segmentation, and evaluate the effect of ego-flow suppression. In the open-set segmentation track we evaluate baseline methods that combine appearance, and geometry to learn prototypes per semantic class. We then compare it to a model that uses an auxiliary contrastive loss to improve the discrimination between known and unknown objects. Datasets and models are publicly released at https://***/vca/.

关键词： Training computer vision Tracking Motion segmentation Semantics Video sequences Benchmark testing

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OmniLayout: Room Layout Reconstruction from Indoor Spherical Panoramas

OmniLayout: Room Layout Reconstruction from Indoor Spherical...

引用

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

作者： Rao, Shivansh Kumar, Vikas Kifer, Daniel Giles, C. Lee Mali, Ankur Penn State Univ University Pk PA 16802 USA

ISBN: (纸本)9781665448994

Given a single RGB panorama, the goal of 3D layout reconstruction is to estimate the room layout by predicting the corners, floor boundary, and ceiling boundary. A common approach has been to use standard convolutional networks to predict the corners and boundaries, followed by post-processing to generate the 3D layout. However, the space-varying distortions in panoramic images are not compatible with the translational equivariance property of standard convolutions, thus degrading performance. Instead, we propose to use spherical convolutions. The resulting network, which we call OmniLayout performs convolutions directly on the sphere surface, sampling according to inverse equirectangular projection and hence invariant to equirectangular distortions. Using a new evaluation metric, we show that our network reduces the error in the heavily distorted regions (near the poles) by approximate to 25% when compared to standard convolutional networks. Experimental results show that OmniLayout outperforms the state-of-the-art by approximate to 4% on two different benchmark datasets (PanoContext and Stanford 2D-3D). Code is available at https://***/rshivansh/OmniLayout.

关键词： Convolutional codes Three-dimensional displays Layout Benchmark testing Distortion Predistortion pattern recognition

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Variational AutoEncoder for Reference based Image Super-Resolution

Variational AutoEncoder for Reference based Image Super-Reso...

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

作者： Liu, Zhi-Song Siu, Wan-Chi Wang, Li-Wen Caritas Inst Higher Educ Hong Kong Peoples R China Hong Kong Polytech Univ Hong Kong Peoples R China

ISBN: (纸本)9781665448994

In this paper, we propose a novel reference based image super-resolution approach via Variational AutoEncoder (RefVAE). Existing state-of-the-art methods mainly focus on single image super-resolution which cannot perform well on large upsampling factors, e.g., 8x. We propose a reference based image super-resolution, for which any arbitrary image can act as a reference for super-resolution. Even using random map or low-resolution image itself the proposed RefVAE can transfer the knowledge from the reference to the super-resolved images. Depending upon different references, the proposed method can generate different versions of super-resolved images from a hidden super-resolution space. Besides using different datasets for some standard evaluations with PSNR and SSIM, we also took part in the NTIRE2021 SR Space challenge [21] and have provided results of the randomness evaluation of our approach. Compared to other state-of-the-art methods, our approach achieves higher diverse scores.

关键词： computer vision Quantization (signal) conferences Computational modeling Superresolution Space exploration pattern recognition

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Single image HDR synthesis using a Densely Connected Dilated ConvNet

Single image HDR synthesis using a Densely Connected Dilated...

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

作者： Akhil, K. A. Jiji, C., V Coll Engn Trivandrum Trivandrum Kerala India

ISBN: (纸本)9781665448994

Visual representations using high dynamic range (HDR) images have become increasingly popular because of their high quality and expressive ability. HDR images are expected to be used in a broad range of applications, including digital cinema, photography, and broadcast. The generation of a HDR image from a single exposure Low Dynamic Range (LDR) image is a challenging task where one must make up for missing data due to underexposure or overexposure and color quantization. In this paper, we propose a deep convolutional neural network (CNN) model with a stack of dilated convolutional blocks for reconstructing a HDR image from a single LDR image. Within each dilation block, the dilation rate of the convolution layer is three and progressively decreases to one. Multiple dilation convolution blocks are further connected densely to improve the representation capacity of the network. As the network is trained in a supervised manner, the additional information is reconstructed from learned features. Our experimental results show that the model effectively captures missing information that was lost from the original image.

关键词： Photography Visualization Quantization (signal) Convolution Dynamic range pattern recognition Convolutional neural networks

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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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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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Comparing Representations in Tracking for Event Camera-based SLAM

Comparing Representations in Tracking for Event Camera-based...

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

作者： Jiao, Jianhao Huang, Huaiyang Li, Liang He, Zhijian Zhu, Yilong Liu, Ming Hong Kong Univ Sci & Technol Hong Kong Peoples R China Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781665448994

This paper investigates two typical image-type representations for event camera-based tracking: time surface (TS) and event map (EM). Based on the original TS-based tracker, we make use of these two representations' complementary strengths to develop an enhanced version. The proposed tracker consists of a general strategy to evaluate the optimization problem's degeneracy online and then switch proper representations. Both TS and EM are motion- and scene-dependent, and thus it is important to figure out their limitations in tracking. We develop six tracker variations and conduct a thorough comparison of them on sequences covering various scenarios and motion complexities. We release our implementations and detailed results to benefit the research community on event cameras: https://***/gogojjh/ESVO_extension.

关键词： Tracking loops computer vision Simultaneous localization and mapping Tracking conferences Switches Trajectory

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End-to-End Learned Image Compression with Augmented Normalizing Flows

End-to-End Learned Image Compression with Augmented Normaliz...

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

作者： Ho, Yung-Han Chan, Chih-Chun Peng, Wen-Hsiao Hang, Hsueh-Ming Natl Chiao Tung Univ Comp Sci Dept Hsinchu Taiwan Natl Chiao Tung Univ Elect Engn Dept Hsinchu Taiwan Natl Chiao Tung Univ Pervas AI Res PAIR Labs Hsinchu Taiwan

ISBN: (纸本)9781665448994

This paper presents a new attempt at using augmented normalizing flows (ANF) for lossy image compression. ANF is a specific type of normalizing flow models that augment the input with an independent noise, allowing a smoother transformation from the augmented input space to the latent space. Inspired by the fact that ANF can offer greater expressivity by stacking multiple variational autoencoders (VAE), we generalize the popular VAE-based compression framework by the autoencoding transforms of ANF. When evaluated on Kodak dataset, our ANF-based model provides 3.4% higher BD-rate saving as compared with a VAE-based baseline that implements hyper-prior with mean prediction. Interestingly, it benefits even more from the incorporation of a post-processing network, showing 11.8% rate saving as compared to 6.0% with the baseline plus post-processing.

关键词： computer vision Image coding conferences Stacking Low-pass filters Transforms Predictive models

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PDAVIS: Bio-inspired Polarization Event Camera

PDAVIS: Bio-inspired Polarization Event Camera

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

作者： Haessig, Germain Joubert, Damien Haque, Justin Milde, Moritz B. Delbruck, Tobi Gruev, Viktor Center for Vision Automation & Control High-Performance Vision Systems AIT Austrian Institute of Technology Vienna Austria University of Zurich Institute of Neuroinformatics ETH Zurich Switzerland Western Sydney University International Centre for Neuromorphic Systems The MARCS Institute Sydney Australia University of Illinois at Urbana-Champaign Department of Electrical and Computer Engineering UrbanaIL United States

ISBN: (纸本)9798350302493

The stomatopod (mantis shrimp) visual system has recently provided a blueprint for the design of paradigm-shifting polarization and multispectral imaging sensors, enabling solutions to challenging medical and remote sensing problems. However, these bioinspired frame-based cameras lack the high dynamic range and asynchronous polarization vision capabilities of the stomatopod visual system, limiting temporal resolution to ~12ms and dynamic range to ~72dB. Here we present a novel stomatopod-inspired polarization camera which mimics the sustained and transient biological visual pathways to save power and sample data beyond the maximum Nyquist frame rate. This bio-inspired sensor simultaneously captures both synchronous intensity frames and asynchronous polarization brightness change information with submillisecond latencies over a millionfold range of illumination. Our PDAVIS camera is comprised of 346x260 pixels, organized in 2-by-2 macropixels, which filter the incoming light with four linear polarization filters offset by 45°. Polarization information is reconstructed using both low-cost and low-latency event-based algorithms and more accurate but slower deep neural networks. Our sensor is used to image high dynamic range polarization scenes that vary at high speeds and to observe the dynamical properties of single collagen fibers in a bovine tendon under rapid cyclical ***: https://***/mFuCeTMWEqY © 2023 ieee.

关键词： Polarization

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