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GraphWalks: Efficient Shape Agnostic Geodesic Shortest Path Estimation

GraphWalks: Efficient Shape Agnostic Geodesic Shortest Path ...

引用

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

作者： Potamias, Rolandos Alexandros Neofytou, Alexandros Bintsi, Kyriaki Margarita Zafeiriou, Stefanos Imperial Coll London London England OCM Digital Media Athens Greece

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Geodesic paths and distances are among the most popular intrinsic properties of 3D surfaces. Traditionally, geodesic paths on discrete polygon surfaces were computed using shortest path algorithms, such as Dijkstra. However, such algorithms have two major limitations. They are non-differentiable which limits their direct usage in learnable pipelines and they are considerably time demanding. To address such limitations and alleviate the computational burden, we propose a learnable network to approximate geodesic paths. The proposed method is comprised by three major components: a graph neural network that encodes node positions in a high dimensional space, a path embedding that describes previously visited nodes and a point classifier that selects the next point in the path. The proposed method provides efficient approximations of the shortest paths and geodesic distances estimations. Given that all of the components of our method are fully differentiable, it can be directly plugged into any learnable pipeline as well as customized under any differentiable constraint. We extensively evaluate the proposed method with several qualitative and quantitative experiments.

关键词： computer vision Three-dimensional displays Shape conferences Pipelines Estimation Approximation algorithms

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Spacing Loss for Discovering Novel Categories

Spacing Loss for Discovering Novel Categories

引用

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

作者： Joseph, K. J. Paul, Sujoy Aggarwal, Gaurav Biswas, Soma Rai, Piyush Han, Kai Balasubramanian, Vineeth N. Indian Inst Technol Hyderabad Hyderabad India Google Res Bengaluru India Indian Inst Sci Bengaluru India Lndian Inst Technol Kanpur Kanpur Uttar Pradesh India Univ Hong Kong Hong Kong Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Novel Class Discovery (NCD) is a learning paradigm, where a machine learning model is tasked to semantically group instances from unlabeled data, by utilizing labeled instances from a disjoint set of classes. In this work, we first characterize existing NCD approaches into single-stage and two-stage methods based on whether they require access to labeled and unlabeled data together while discovering new classes. Next, we devise a simple yet powerful loss function that enforces separability in the latent space using cues from multi-dimensional scaling, which we refer to as Spacing Loss. Our proposed formulation can either operate as a standalone method or can be plugged into existing methods to enhance them. We validate the efficacy of Spacing Loss with thorough experimental evaluation across multiple settings on CIFAR-10 and CIFAR-100 datasets.

关键词： Training computer vision conferences Computational modeling Machine learning Benchmark testing Data models

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SeeTheSeams: Localized Detection of Seam Carving based Image Forgery in Satellite Imagery

SeeTheSeams: Localized Detection of Seam Carving based Image...

引用

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

作者： Gudavalli, Chandrakanth Rosten, Erik Nataraj, Lakshmanan Chandrasekaran, Shivkumar Manjunath, B. S. Mayachitra Inc Santa Barbara CA 93111 USA UC Santa Barbara Elect & Comp Engn Dept Santa Barbara CA USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Seam carving is a popular technique for content aware image retargeting. It can be used to deliberately manipulate images, for example, change the GPS locations of a building or displace/remove roads in a satellite image. This paper proposes a novel approach for detecting and localizing seams in such images. While there are methods to detect seam carving based manipulations, this is the first time that robust localization and detection of seam carving forgery is made possible. We also propose a seam localization score (SLS) metric to evaluate the effectiveness of localization. The proposed method is evaluated extensively on a large collection of images from different sources, demonstrating a high level of detection and localization performance across these datasets. The code and datasets curated during this work will be released to the public.

关键词： Location awareness Measurement computer vision Satellites Image coding Roads conferences

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AuxMix: Semi-Supervised Learning with Unconstrained Unlabeled Data

AuxMix: Semi-Supervised Learning with Unconstrained Unlabele...

引用

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

作者： Banitalebi-Dehkordi, Amin Gujjar, Pratik Zhang, Yong Huawei Technol Canada Co Ltd Vancouver BC Canada

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Semi-supervised learning (SSL) has seen great strides when labeled data is scarce but unlabeled data is abundant. Critically, most recent work assume that such unlabeled data is drawn from the same distribution as the labeled data. In this work, we show that state-of-the-art SSL algorithms suffer a degradation in performance in the presence of unlabeled auxiliary data that does not necessarily possess the same class distribution as the labeled set. We term this problem as Auxiliary-SSL and propose AuxMix, an algorithm that leverages self-supervised learning tasks to learn generic features in order to mask auxiliary data that are not semantically similar to the labeled set. We also propose to regularize learning by maximizing the predicted entropy for dissimilar auxiliary samples. We show an improvement of 5% over existing baselines on a ResNet-50 model when trained on CIFAR10 dataset with 4k labeled samples and all unlabeled data is drawn from the Tiny-Imagenet dataset. We report competitive results on several datasets and conduct ablation studies.

关键词： Degradation computer vision conferences Self-supervised learning Semisupervised learning Prediction algorithms Entropy

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Asymmetric Information Distillation Network for Lightweight Super Resolution

Asymmetric Information Distillation Network for Lightweight ...

引用

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

作者： Zong, Zhikai Zha, Lin Jiang, Jiande Liu, Xiaoxiao Qingdao Hiimage Technol Co Ltd Hisense Visual Technol Co Ltd Qingdao Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The purpose of this paper is to design a lightweight network to achieve image super resolution performance equivalent to SRResNet. We design an asymmetric information distillation block (AIDB) with distillation information multiplexing and asymmetric information extraction capabilities to better achieve this goal. Distillation information multiplexing refers to the repeated processing of distilled information to supplement the ability of key information extraction. Asymmetric information enhancement block (AIEB) refers to identify different features in the image by the horizontal and vertical feature extraction. AIEB greatly reduces the number of parameters, and distillation information multiplexing works as a supplement to the lost high dimensional information. A large number of experiments show that our asymmetric information distillation network (AIDN) achieves a better balance of performance and complexity than SOTA model. Moreover, Our proposed AIDN ranked second in the model complexity track of NTIRE2022 efficient super resolution challenge. Compared with the first place in this track, we achieves higher PSNR performance on testset with a slight disadvantage in the number of parameters. The code is available at https://***/zzksdu/AIDN.

关键词： Multiplexing Measurement computer vision Image resolution Computational modeling conferences Feature extraction

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Learned Low Bitrate Video Compression with Space-Time Super-Resolution

Learned Low Bitrate Video Compression with Space-Time Super-...

引用

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

作者： Yang, Jiayu Yang, Chunhui Xiong, Fei Wang, Feng Wang, Ronggang Peking Univ Shenzhen Grad Sch Shenzhen Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

This paper presents a learned low bitrate video compression framework that consists of pre-processing, compression and post-processing. In pre-processing stage, the source videos are optionally reduced to low-resolution or low-frame-rate ones to better meet with the limited bandwidth. In compression stage, inter-frame prediction is performed by deformable convolution (DCN). The predicted frame is then used as temporal conditions to compress the current frame. In post-processing stage, the decoded videos are fed into a Space-Time Super-Resolution module, in which the videos are restored to original spatial and temporal resolutions. Experimental results on CLIC22 video test conditions demonstrate that the proposed method shows better performance on both objective and subjective quality at low bitrate. Our team name is PKUSZ-LVC.

关键词： computer vision Convolution conferences Computational modeling Superresolution Bit rate Video compression

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Generating Adversarial Samples in Mini-Batches May Be Detrimental To Adversarial Robustness

Generating Adversarial Samples in Mini-Batches May Be Detrim...

引用

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

作者： Redgrave, Timothy Crum, Colton University of Notre Dame Department of Computer Science and Engineering Notre DameIN46556 United States

ISBN: (纸本)9798350302493

Neural networks have been proven to be both highly effective within computer vision, and highly vulnerable to adversarial attacks. Consequently, as the use of neural networks increases due to their unrivaled performance, so too does the threat posed by adversarial attacks. In this work, we build towards addressing the challenge of adversarial robustness by exploring the relationship between the mini-batch size used during adversarial sample generation and the strength of the adversarial samples produced. We demonstrate that an increase in mini-batch size results in a decrease in the efficacy of the samples produced, and we draw connections between these observations and the phenomenon of vanishing gradients. Next, we formulate loss functions such that adversarial sample strength is not degraded by mini-batch size. Our findings highlight a potential risk for underestimating the true (practical) strength of adversarial attacks, and a risk of overestimating a model's robustness. We share our codes to let others replicate our experiments and to facilitate further exploration of the connections between batch size and adversarial sample strength. © 2023 ieee.

关键词： computer vision

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What Do You See in Vehicle? Comprehensive vision Solution for In-Vehicle Gaze Estimation

What Do You See in Vehicle? Comprehensive Vision Solution fo...

引用

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

作者： Cheng, Yihua Zhu, Yaning Wang, Zongji Hao, Hongquan Liu, Yongwei Cheng, Shiqing Wang, Xi Chang, Hyung Jin Univ Birmingham Birmingham W Midlands England Huazhong Univ Sci & Technol Wuhan Hubei Peoples R China Chinese Acad Sci NIST Beijing Peoples R China CalmCar Suzhou Jiangsu Peoples R China

ISBN: (纸本)9798350353013;9798350353006

Driver's eye gaze holds a wealth of cognitive and intentional cues crucial for intelligent vehicles. Despite its significance, research on in-vehicle gaze estimation remains limited due to the scarcity of comprehensive and well-annotated datasets in real driving scenarios. In this paper, we present three novel elements to advance in-vehicle gaze research. Firstly, we introduce IVGaze, a pioneering dataset capturing in-vehicle gaze, collected from 125 subjects and covering a large range of gaze and head poses within vehicles. In this dataset, we propose a new vision-based solution for in-vehicle gaze collection, introducing a refined gaze target calibration method to tackle annotation challenges. Second, our research focuses on in-vehicle gaze estimation leveraging the IVGaze. In-vehicle face images often suffer from low resolution, prompting our introduction of a gaze pyramid transformer that leverages transformer-based multilevel features integration. Expanding upon this, we introduce the dual-stream gaze pyramid transformer (GazeDPTR). Employing perspective transformation, we rotate virtual cameras to normalize images, utilizing camera pose to merge normalized and original images for accurate gaze estimation. GazeDPTR shows state-of-the-art performance on the IVGaze dataset. Thirdly, we explore a novel strategy for gaze zone classification by extending the GazeDPTR. A foundational tri-plane and project gaze onto these planes are newly defined. Leveraging both positional features from the projection points and visual attributes from images, we achieve superior performance compared to relying solely on visual features, substantiating the advantage of gaze estimation. The project is available at https://***/work/ivgaze.

关键词： Image annotation

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An Effective Framework of Multi-Class Product Counting and recognition for Automated Retail Checkout

An Effective Framework of Multi-Class Product Counting and R...

引用

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

作者： Wan, Junfeng Qian, Shuhao Tian, Zihan Zhao, Yanyun Beijing Univ Posts & Telecommun Beijing Peoples R China Beijing Key Lab Network Syst & Network Culture Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

As the field of computer vision grows, Automated Retail Checkout has become a highly anticipated development goal. The key of this task is to improve the accuracy rate. If there is an error, it will bring serious losses to the business and awful experience for customers which is not our expected. This competition gives us an opportunity to simulate check-out in a real world scenario, so that we can identify problems and solve them, not only for the competition, but also for the practical application. As one of the participating teams in this task, we pursue the goal of avoiding misdetection and misclassification, and build a complete set of framework to achieve high-precise, high-recall performance. In addition, there is an excessive difference between the training data and test data. How to use limited data to make up for the differences in this part is also one of the highlights of our framework. In general, our framework consists of three main parts. Firstly, the Pre-Processing module to make up for the differences between training and test data. The DTC module completes the overall process of automatic recognition. Finally the MTCR module is proposed to post-process the output of the DTC module. On the TestA data of AICITY2022 Task 4, we have achieved significant result compared to the other teams. Finally, our model is ranked 1st in AICITY2022 Task 4 [17].

关键词： Training computer vision Codes conferences Training data Trajectory pattern recognition

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PEA: Improving the Performance of ReLU Networks for Free by Using Progressive Ensemble Activations

PEA: Improving the Performance of ReLU Networks for Free by ...

引用

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

作者： Utasi, Akos Continental AI Dev Ctr Budapest Hungary

ISBN: (纸本)9781665487399

In recent years novel activation functions have been proposed to improve the performance of neural networks, and they show superior performance compared to the ReLU counterpart. However, there are environments, where the availability of complex activations is limited, and usually only the ReLU is supported. In this paper we propose methods that can be used to improve the performance of ReLU networks by using these efficient novel activations during model training. More specifically, we propose ensemble activations that are composed of the ReLU and one of these novel activations. Furthermore, the coefficients of the ensemble are neither fixed nor learned, but are progressively updated during the training process in a way that by the end of the training only the ReLU activations remain active in the network and the other activations can be removed. This means that in inference time the network contains ReLU activations only. We perform extensive evaluations on the ImageNet classification task using various compact network architectures and various novel activation functions. Results show 0.2-0.8% top-1 accuracy gain, which confirms the applicability of the proposed methods. Furthermore, we demonstrate the proposed methods on semantic segmentation and we boost the performance of a compact segmentation network by 0.34% mIOU on the Cityscapes dataset.

关键词： Training Image segmentation computer vision conferences Semantics Neural networks Network architecture

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