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InstancePose: Fast 6DoF Pose Estimation for Multiple Objects from a Single RGB Image 18

InstancePose: Fast 6DoF Pose Estimation for Multiple Objects...

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18th ieee/CVF International conference on computer vision (ICCV)

作者： Aing, Lee Lie, Wen-Nung Chiang, Jui-Chiu Lin, Guo-Shiang Natl Chung Cheng Univ CCU Dept Elect Engn Minxiong Township Chiayi Taiwan Natl Chung Cheng Univ CCU Ctr Innovat Res Aging Soc CIRAS Minxiong Township Chiayi Taiwan Natl Chung Cheng Univ CCU Adv Inst Mfg High Tech Innovat AIM HI Minxiong Township Chiayi Taiwan Natl Chin Yi Univ Technol Dept Comp Sci & Informat Engn Taichung Taiwan

ISBN: (纸本)9781665401913

6DoF object pose estimation depends on positional accuracy, implementation complexity and processing speed. This study presents a method to estimate 6DoF object poses for multi-instance object detection that requires less time and is accurate. The proposed method uses a deep neural network, which outputs 4 types of feature maps: the error object mask, semantic object masks, center vector maps (CVM) and 6D coordinate maps. These feature maps are combined in post processing to detect and estimate multi-object 2D-3D correspondences in parallel for PnP RANSAC estimation. The experiments show that the method can process input RGB images containing 7 different object categories/ instances at a speed of 25 frames per second with competitive accuracy, compared with current state-of-the-art methods, which focus only on some specific conditions.

关键词： Deep learning Image segmentation computer vision Three-dimensional displays conferences Semantics Pose estimation

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Proceedings - 2023 22nd ieee/WIC International conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2023

Proceedings - 2023 22nd IEEE/WIC International Conference on...

引用

22nd ieee/WIC International conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2023

ISBN: (纸本)9798350309188

The proceedings contain 100 papers. The topics discussed include: joint constrained learning for causal event-event relation extraction of brain connectome;vision-based automatic groceries tracking system - smart homes;measuring vagueness and subjectivity in texts: from symbolic to neural VAGO;proposing a new security game with reward and penalty;web content filtering through knowledge distillation of large language models;detecting inactive cyberwarriors from online forums;visual insights: enhancing online news maps with relevant illustrations;RescueRoute: using real-time traffic data for routing emergency vehicles;a comparative study of water temperature forecasting methods;FairGridSearch: a framework to compare fairness-enhancing models;and multi-behavior recommendation with action pattern-aware networks.

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The Role of 'Sign' and 'Direction' of Gradient on the Performance of CNN

The Role of 'Sign' and 'Direction' of Gradient on the Perfor...

引用

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

作者： Agarwal, Akshay Singh, Richa Vatsa, Mayank IIIT Delhi Delhi India IIT Jodhpur Jodhpur Rajasthan India

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193601

State-of-the-art deep learning models have achieved superlative performance across multiple computer vision applications such as object recognition, face recognition, and digits/character classification. Most of these models highly rely on the gradient information flows through the network for learning. By utilizing this gradient information, a simple gradient sign method based attack is developed to fool the deep learning models. However, the primary concern with this attack is the perceptibility of noise for large degradation in classification accuracy. This research address the question of whether an imperceptible gradient noise can be generated to fool the deep neural networks? For this, the role of sign function in the gradient attack is analyzed. The analysis shows that without-sign function, i.e. gradient magnitude, not only leads to a successful attack mechanism but the noise is also imperceptible to the human observer. Extensive quantitative experiments performed using two convolutional neural networks validate the above observation. For instance, AlexNet architecture yields 63.54% accuracy on the CIFAR-10 database which reduces to 0.0% and 26.39% when sign (i.e., perceptible) and without-sign (i.e., imperceptible) of the gradient is utilized, respectively. Further, the role of the direction of the gradient for image manipulation is studied. When an image is manipulated in the positive direction of the gradient, an adversarial image is generated. On the other hand, if the opposite direction of the gradient is utilized for image manipulation, it is observed that the classification error rate of the CNN model is reduced. On AlexNet, the error rate of 36.46% reduces to 4.29% when images of CIFAR-10 are manipulated in the negative direction of the gradient. To explore other enthusiastic results on multiple object databases, including CIFAR-100, fashion-MNIST, and SVHN, please refer to the full paper.

关键词： Databases Machine learning Computational modeling Object recognition computer vision Training Optimization

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AdaMT-Net: An Adaptive Weight Learning Based Multi-Task Learning Model For Scene Understanding

AdaMT-Net: An Adaptive Weight Learning Based Multi-Task Lear...

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

作者： Jha, Ankit Kumar, Awanish Banerjee, Biplab Chaudhuri, Subhasis Indian Inst Technol Dept Elect Engn Mumbai Maharashtra India Indian Inst Technol Ctr Studies Resources Engn Mumbai Maharashtra India

ISBN: (纸本)9781728193601

We tackle the problem of deep end-to-end multi-task learning (MTL) for visual scene understanding from monocular images in this paper. It is proven that learning several related tasks together helps in attaining improved performance per-task than training them independently. This is due to the fact that related tasks share important feature characteristics among themselves, which the MTL techniques can effectively explore for improved joint training. Based on this premise, we are interested in generic to specific feature extraction given the different tasks within a common framework. To this end, we propose a typical U-Net based encoder-decoder architecture called AdaMT-Net, where the densely-connected deep convolutional neural network (CNN) based feature encoder is shared among the tasks while the soft-attention based task-specific decoder modules produce the desired outcomes. One major issue in MTL is to assign the weights for the task-specific loss-terms in the final cumulative optimization function. As opposed to the manual approach, we propose a novel adaptive weight learning strategy by carefully exploring the loss-gradients per-task over the training iterations. Experimental results on the benchmark CityScapes, NYUv2, and ISPRS datasets confirm that AdaMT-Net achieves state-of-the-art performance on most of the evaluation metrics.

关键词： computer vision

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Less is More: Proxy Datasets in NAS approaches

Less is More: Proxy Datasets in NAS approaches

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Brian Moser Federico Raue Jö rn Hees Andreas Dengel German Research Center for Artificial Intelligence (DFKI) Germany TU Kaiserslautern Germany

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Neural Architecture Search (NAS) defines the design of Neural Networks as a search problem. Unfortunately, NAS is computationally intensive because of various possibilities depending on the number of elements in the design and the possible connections between them. In this work, we extensively analyze the role of the dataset size based on several sampling approaches for reducing the dataset size (unsupervised and supervised cases) as an agnostic approach to reduce search time. We compared these techniques with four common NAS approaches in NAS-Bench201 in roughly 1,400 experiments on CIFAR-100. One of our surprising findings is that in most cases we can reduce the amount of training data to 25%, consequently also reducing search time to 25%, while at the same time maintaining the same accuracy as if training on the full dataset. In addition, some designs derived from subsets out-perform designs derived from the full dataset by up to 22 p.p. accuracy.

关键词： Training computer vision conferences Neural networks Training data computer architecture Search problems

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CoRe: Color Regression for Multicolor Fashion Garments

CoRe: Color Regression for Multicolor Fashion Garments

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Alexandre Ramé Arthur Douillard Charles Ollion Sorbonne Universit&#x00E9Heuritech Heuritech

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Developing deep networks that analyze fashion garments has many real-world applications. Among all fashion attributes, color is one of the most important yet challenging to detect. Existing approaches are classification-based and thus cannot go beyond the list of discrete predefined color names. In this paper, we handle color detection as a regression problem to predict the exact RGB values. That’s why in addition to a first color classifier, we include a second regression stage for refinement in our newly proposed architecture. This second step combines two attention models: the first depends on the type of clothing, the second depends on the color previously detected by the classifier. Our final prediction is the weighted spatial pooling over the image pixels RGB values, where the illumination has been corrected. This architecture is modular and easily expanded to detect the RGBs of all colors in a multicolor garment. In our experiments, we show the benefits of each component of our architecture.

关键词： Visualization computer vision Image color analysis conferences Clothing Lighting computer architecture

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Analysis and Extensions of Adversarial Training for Video Classification

Analysis and Extensions of Adversarial Training for Video Cl...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Kaleab A. Kinfu René Vidal Mathematical Institute for Data Science Johns Hopkins University Baltimore MD USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Adversarial training (AT) is a simple yet effective defense against adversarial attacks to image classification systems, which is based on augmenting the training set with attacks that maximize the loss. However, the effectiveness of AT as a defense for video classification has not been thoroughly studied. Our first contribution is to show that generating optimal attacks for video requires carefully tuning the attack parameters, especially the step size. Notably, we show that the optimal step size varies linearly with the attack budget. Our second contribution is to show that using a smaller (sub-optimal) attack budget at training time leads to a more robust performance at test time. Based on these findings, we propose three defenses against attacks with variable attack budgets. The first one, Adaptive AT, is a technique where the attack budget is drawn from a distribution that is adapted as training iterations proceed. The second, Curriculum AT, is a technique where the attack budget is increased as training iterations proceed. The third, Generative AT, further couples AT with a denoising generative adversarial network to boost robust performance. Experiments on the UCF101 dataset demonstrate that the proposed methods improve adversarial robustness against multiple attack types.

关键词： Training computer vision conferences Noise reduction Generative adversarial networks Robustness pattern recognition

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Focus on Content not Noise: Improving Image Generation for Nuclei Segmentation by Suppressing Steganography in CycleGAN

Focus on Content not Noise: Improving Image Generation for N...

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International conference on computer vision workshops (ICCV workshops)

作者： Jonas Utz Tobias Weise Maja Schlereth Fabian Wagner Mareike Thies Mingxuan Gu Stefan Uderhardt Katharina Breininger Department Artificial Intelligence in Biomedical Engineering Friedrich-Alexander-Universität Erlangen-Nürnberg Germany Pattern Recognition Lab FAU Erlangen-Nürnberg Germany Department of Medicine 3 - Rheumatology and Immunology FAU Erlangen-Nürnberg Germany Deutsches Zentrum für Immuntherapie FAU Erlangen-Nürnberg Germany Exploratory Research Unit Optical Imaging Centre Erlangen FAU Erlangen-Nürnberg Germany

Annotating nuclei in microscopy images for the training of neural networks is a laborious task that requires expert knowledge and suffers from inter- and intra-rater variability, especially in fluorescence microscopy. Generative networks such as CycleGAN can inverse the process and generate synthetic microscopy images for a given mask, thereby building a synthetic dataset. However, past works report content inconsistencies between the mask and generated image, partially due to CycleGAN minimizing its loss by hiding shortcut information for the image reconstruction in high frequencies rather than encoding the desired image content and learning the target task. In this work, we propose to remove the hidden shortcut information, called steganography, from generated images by employing a low pass filtering based on the discrete cosine transform (DCT). We show that this increases coherence between generated images and cycled masks and evaluate synthetic datasets on a downstream nuclei segmentation task. Here we achieve an improvement of 5.4 percentage points in the F1-score compared to a vanilla CycleGAN. Integrating advanced regularization techniques into the CycleGAN architecture may help mitigate steganography-related issues and produce more accurate synthetic datasets for nuclei segmentation.

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Towards a Deeper Understanding of Skeleton-based Gait recognition

Towards a Deeper Understanding of Skeleton-based Gait Recogn...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Torben Teepe Johannes Gilg Fabian Herzog Stefan Hö rmann Gerhard Rigoll Technical University of Munich

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Gait recognition is a promising biometric with unique properties for identifying individuals from a long distance by their walking patterns. In recent years, most gait recognition methods used the person’s silhouette to extract the gait features. However, silhouette images can lose fine-grained spatial information, suffer from (self) occlusion, and be challenging to obtain in real-world scenarios. Furthermore, these silhouettes also contain other visual clues that are not actual gait features and can be used for identification, but also to fool the system. Model-based methods do not suffer from these problems and are able to represent the temporal motion of body joints, which are actual gait features. The advances in human pose estimation started a new era for model-based gait recognition with skeleton-based gait recognition. In this work, we propose an approach based on Graph Convolutional Networks (GCNs) that combines higher-order inputs, and residual networks to an efficient architecture for gait recognition. Extensive experiments on the two popular gait datasets, CASIA-B and OUMVLP-Pose, show a massive improvement (3×) of the state-of-the-art (SotA) on the largest gait dataset OUMVLP-Pose and strong temporal modeling capabilities. Finally, we visualize our method to understand skeleton-based gait recognition better and to show that we model real gait features.

关键词： Training Visualization Pose estimation computer architecture Performance gain Feature extraction pattern recognition

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PKU-DyMVHumans: A Multi-View Video Benchmark for High-Fidelity Dynamic Human Modeling

PKU-DyMVHumans: A Multi-View Video Benchmark for High-Fideli...

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conference on computer vision and pattern recognition (CVPR)

作者： Xiaoyun Zheng Liwei Liao Xufeng Li Jianbo Jiao Rongjie Wang Feng Gao Shiqi Wang Ronggang Wang Peking University Shenzhen Graduate School Peng Cheng Laboratory City University of Hong Kong University of Birmingham Peking University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

High-quality human reconstruction and photo-realistic rendering of a dynamic scene is a long-standing problem in computer vision and graphics. Despite considerable ef-forts invested in developing various capture systems and re-construction algorithms, recent advancements still struggle with loose or oversized clothing and overly complex poses. In part, this is due to the challenges of acquiring high-quality human datasets. To facilitate the development of these fields, in this paper, we present PKU-DyMVHumans, a versatile human-centric dataset for high-fidelity reconstruction and rendering of dynamic human scenarios from dense multi-view videos. It comprises 8.2 million frames captured by more than 56 synchronized cameras across diverse scenarios. These sequences comprise 32 human subjects across 45 different scenarios, each with a high-detailed appearance and realistic human motion. Inspired by recent advancements in neural radiance field (NeRF)-based scene representations, we carefully set up an off-the-shelf framework that is easy to provide those state-of-the-art NeRF-based implementations and benchmark on PKU-DyMVHumans dataset. It is paving the way for various applications like fine-grained foreground/background de-composition, high-quality human reconstruction and photo-realistic novel view synthesis of a dynamic scene. Exten-sive studies are performed on the benchmark, demonstrating new observations and challenges that emerge from using such high-fidelity dynamic data. The project page and data is available at: https://***.

关键词： computer vision Heuristic algorithms Dynamics Lighting Benchmark testing Rendering (computer graphics) Neural radiance field

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