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Interpolation-Based Event Visual Data Filtering Algorithms

Interpolation-Based Event Visual Data Filtering Algorithms

引用

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

作者： Marcin Kowalczyk Tomasz Kryjak Embedded Vision Systems Group AGH University of Krakow

The field of neuromorphic vision is developing rapidly, and event cameras are finding their way into more and more applications. However, the data stream from these sensors is characterised by significant noise. In this paper, we propose a method for event data that is capable of removing approximately 99% of noise while preserving the majority of the valid signal. We have proposed four algorithms based on the matrix of infinite impulse response (IIR) filters method. We compared them on several event datasets that were further modified by adding artificially generated noise and noise recorded with dynamic vision sensor. The proposed methods use about 30KB of memory for a sensor with a resolution of 1280 × 720 and is therefore well suited for implementation in embedded devices.

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Boosting Adversarial Robustness using Feature Level Stochastic Smoothing

Boosting Adversarial Robustness using Feature Level Stochast...

引用

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

作者： Addepalli, Sravanti Jain, Samyak Sriramanan, Gaurang Babu, R. Venkatesh Indian Inst Sci Video Analyt Lab Dept Computat & Data Sci Bangalore Karnataka India

ISBN: (纸本)9781665448994

Advances in adversarial defenses have led to a significant improvement in the robustness of Deep Neural Networks. However, the robust accuracy of present state-of-the-art defenses is far from the requirements in critical applications such as robotics and autonomous navigation systems. Further, in practical use cases, network prediction alone might not suffice, and assignment of a confidence value for the prediction can prove crucial. In this work, we propose a generic method for introducing stochasticity in the network predictions, and utilize this for smoothing decision boundaries and rejecting low confidence predictions, thereby boosting the robustness on accepted samples. The proposed Feature Level Stochastic Smoothing based classification also results in a boost in robustness without rejection over existing adversarial training methods. Finally, we combine the proposed method with adversarial detection methods, to achieve the benefits of both approaches.

关键词： Training Deep learning computer vision Smoothing methods conferences Boosting Feature extraction

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InfoScrub: Towards Attribute Privacy by Targeted Obfuscation

InfoScrub: Towards Attribute Privacy by Targeted Obfuscation

引用

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

作者： Wang, Hui-Po Orekondy, Tribhuvanesh Fritz, Mario CISPA Helmholtz Ctr Informat Secur Saarbrucken Germany Max Planck Inst Informat Saarland Informat Campus Saarbrucken Germany

ISBN: (纸本)9781665448994

Personal photos of individuals when shared online, apart from exhibiting a myriad of memorable details, also reveals a wide range of private information and potentially entails privacy risks (e.g., online harassment, tracking). To mitigate such risks, it is crucial to study techniques that allow individuals to limit the private information leaked in visual data. We tackle this problem in a novel image obfuscation framework: to maximize entropy on inferences over targeted privacy attributes, while retaining image fidelity. We approach the problem based on an encoder-decoder style architecture, with two key novelties: (a) introducing a discriminator to perform bi-directional translation simultaneously from multiple unpaired domains;(b) predicting an image interpolation which maximizes uncertainty over a target set of attributes. We find our approach generates obfuscated images faithful to the original input images and additionally increases uncertainty by 6.2x (or up to 0.85 bits) over the non-obfuscated counterparts.

关键词： Privacy Visualization Interpolation computer vision Uncertainty Target tracking conferences

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Differentiable Rendering-based Pose-Conditioned Human Image Generation

Differentiable Rendering-based Pose-Conditioned Human Image ...

引用

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

作者： Horiuchi, Yusuke Simo-Serra, Edgar Iizuka, Satoshi Ishikawa, Hiroshi Waseda Univ Tokyo Japan Univ Tsukuba Tsukuba Ibaraki Japan

ISBN: (纸本)9781665448994

Conditional human image generation, or generation of human images with specified pose based on one or more reference images, is an inherently ill-defined problem, as there can be multiple plausible appearance for parts that are occluded in the reference. Using multiple images can mitigate this problem while boosting the performance. In this work, we introduce a differentiable vertex and edge renderer for incorporating the pose information to realize human image generation conditioned on multiple reference images. The differentiable renderer has parameters that can be jointly optimized with other parts of the system to obtain better results by learning more meaningful shape representation of human pose. We evaluate our method on the Market-1501 and DeepFashion datasets and comparison with existing approaches validates the effectiveness of our approach.

关键词： Heating systems computer vision Image synthesis Shape Image edge detection conferences Training data

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Single View Geocentric Pose in the Wild

Single View Geocentric Pose in the Wild

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

作者： Christie, Gordon Foster, Kevin Hagstrom, Shea Hager, Gregory D. Brown, Myron Z. Johns Hopkins Univ Appl Phys Lab Baltimore MD 21218 USA Johns Hopkins Univ Dept Comp Sci Baltimore MD 21218 USA

ISBN: (纸本)9781665448994

Current methods for Earth observation tasks such as semantic mapping, map alignment, and change detection rely on near-nadir images;however, often the first available images in response to dynamic world events such as natural disasters are oblique. These tasks are much more difficult for oblique images due to observed object parallax. There has been recent success in learning to regress an object's geocentric pose, defined as height above ground and orientation with respect to gravity, by training with airborne lidar registered to satellite images. We present a model for this novel task that exploits affine invariance properties to outperform state of the art performance by a wide margin. We also address practical issues required to deploy this method in the wild for real-world applications. Our data and code are publicly available(1).

关键词： Training Earth computer vision Satellites Laser radar conferences Semantics

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Graph-based Person Signature for Person Re-Identifications

Graph-based Person Signature for Person Re-Identifications

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

作者： Nguyen, Binh X. Nguyen, Binh D. Do, Tuong Tjiputra, Erman Tran, Quang D. Nguyen, Anh AIOZ Singapore Singapore Imperial Coll London London England

ISBN: (纸本)9781665448994

The task of person re-identification (ReID) is to match images of the same person over multiple non-overlapping camera views. Due to the variations in visual factors, previous works have investigated how the person identity, body parts, and attributes benefit the person ReID problem. However, the correlations between attributes, body parts, and within each attribute are not fully utilized. In this paper, we propose a new method to effectively aggregate detailed person descriptions (attributes labels) and visual features (body parts and global features) into a graph, namely Graph-based Person Signature, and utilize Graph Convolutional Networks to learn the topological structure of the visual signature of a person. The graph is integrated into a multi-branch multi-task framework for person re-identification. The extensive experiments are conducted to demonstrate the effectiveness of our proposed approach on two large-scale datasets, including Market-1501 and DukeMTMC-ReID. Our approach achieves competitive results among the state of the art and outperforms other attribute-based or mask-guided methods. Source available at https://***/aioz-ai/cvprw21_GPS.

关键词： Visualization computer vision Correlation conferences Aggregates Benchmark testing Cameras

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A Strong Baseline for Vehicle Re-Identification

A Strong Baseline for Vehicle Re-Identification

引用

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

作者： Huynh, Su, V Nguyen, Nam H. Nguyen, Ngoc T. Nguyen, Vinh T. Q. Huynh, Chau Chuong Nguyen Cybercore AI Ho Chi Minh City Vietnam

ISBN: (纸本)9781665448994

Vehicle Re-Identification (Re-ID) aims to identify the same vehicle across different cameras, hence plays an important role in modern traffic management systems. The technical challenges require the algorithms must be robust in different views, resolution, occlusion and illumination conditions. In this paper, we first analyze the main factors hindering the Vehicle Re-ID performance. We then present our solutions, specifically targeting the dataset Track 2 of the 5th AI City Challenge, including (1) reducing the domain gap between real and synthetic data, (2) network modification by stacking multi heads with attention mechanism, (3) adaptive loss weight adjustment. Our method achieves 61.34% mAP on the private CityFlow testset without using external dataset or pseudo labeling, and outperforms all previous works at 87.1% mAP on the Veri benchmark. The code is available at https://***/cybercore-co-ltd/track2_aicity_2021.

关键词： Training computer vision Target tracking Head conferences Urban areas Stacking

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Efficient Space-time Video Super Resolution using Low-Resolution Flow and Mask Upsampling

Efficient Space-time Video Super Resolution using Low-Resolu...

引用

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

作者： Dutta, Saikat Shah, Nisarg A. Mittal, Anurag IIT Madras Chennai Tamil Nadu India IIT Jodhpur Karwar Rajasthan India

ISBN: (纸本)9781665448994

This paper explores an efficient solution for Space-time Super-Resolution, aiming to generate High-resolution Slow-motion videos from Low Resolution and Low Frame rate videos. A simplistic solution is the sequential running of Video Super Resolution and Video Frame interpolation models. However, this type of solutions are memory inefficient, have high inference time, and could not make the proper use of space-time relation property. To this extent, we first interpolate in LR space using quadratic modeling. Input LR frames are super-resolved using a state-of-the-art Video Super-Resolution method. Flowmaps and blending mask which are used to synthesize LR interpolated frame is reused in HR space using bilinear upsampling. This leads to a coarse estimate of HR intermediate frame which often contains artifacts along motion boundaries. We use a refinement network to improve the quality of HR intermediate frame via residual learning. Our model is lightweight and performs better than current state-of-the-art models in REDS STSR Validation set.

关键词： Interpolation computer vision conferences Superresolution Refining pattern recognition Videos

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

Hierarchical Spatial Pyramid Network For Cervical Precancero...

引用

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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Boosting Unconstrained Face recognition with Auxiliary Unlabeled Data

Boosting Unconstrained Face Recognition with Auxiliary Unlab...

引用

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

作者： Shi, Yichun Jain, Anil K. Michigan State Univ E Lansing MI 48824 USA

ISBN: (纸本)9781665448994

In recent years, significant progress has been made in face recognition, which can be partially attributed to the availability of large-scale labeled face datasets. However, since the faces in these datasets usually contain limited degree and types of variation, the resulting trained models generalize poorly to more realistic unconstrained face datasets. While collecting labeled faces with larger variations could be helpful, it is practically infeasible due to privacy and labor cost. In comparison, it is easier to acquire a large number of unlabeled faces from different domains, which could be used to regularize the learning of face representations. We present an approach to use such unlabeled faces to learn generalizable face representations, where we assume neither the access to identity labels nor domain labels for unlabeled images. Experimental results on unconstrained datasets show that a small amount of unlabeled data with sufficient diversity can (i) lead to an appreciable gain in recognition performance and (ii) outperform the supervised baseline when combined with less than half of the labeled data. Compared with the state-of-the-art face recognition methods, our method further improves their performance on challenging benchmarks, such as IJBB, IJB-C and IJB-S.

关键词： Training Visualization Privacy computer vision Protocols Face recognition conferences

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