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A Novel Robust Color Tag for a Highly Efficient Soccer Robot vision System 22

A Novel Robust Color Tag for a Highly Efficient Soccer Robot...

引用

22nd ieee/ACIS International conference on computer and Information Science, ICIS 2022

作者： Sun, Yan Sun, Shaorong Shanghai University School of Computer Engineering and Science Shanghai China University of Shanghai for Science and Technology Business School Shanghai China

ISBN: (数字)9781665494632

ISBN: (纸本)9781665494632

Soccer robot league is a competition on the cooperation between multi-robots in a highly dynamic environment. Getting accurate and precise information on time in a computationally constrained environment is challenging. In the small size league, each robot has a unique colored tag on the top for identification. Traditional color tag maximizes the colored pattern to give a stable recognition result, however, it might cause recognition confusion in the competitive game. This paper proposes a novel rotation-invariant tag to avoid the confusion without extra recognition constrains. A highly efficient vision system is constructed, each module is optimized to improve the recognition efficiency and robustness. The system focuses on the small size league but capable to be transferred to other leagues. The experiments evaluate the time consumption of each part and the whole system. The results demonstrate that recognizing the robot through our novel identity tag by the highly optimized system has achieved the real-time standard. © 2022 ieee.

关键词： Color

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Stacked Ensemble of Convolutional Neural Networks for Follicles Detection on Scalp Images 21st

Stacked Ensemble of Convolutional Neural Networks for Follic...

引用

21st International conference on Artificial Intelligence and Soft Computing (ICAISC)

作者： Ruta, Dymitr Cen, Ling Ruta, Andrzej Vu, Quang Hieu Khalifa Univ EBTIC Abu Dhabi U Arab Emirates PLACEMAKE IO London England Zalora Data Sci Grp Singapore Singapore

ISBN: (纸本)9783031234798;9783031234804

An average person's head is covered with up to 100000 hairs growing out of follicular openings on the scalp's skin. Automated hair therapy requires precise detection and localization of follicles on the scalp and still poses a significant challenge for the computer vision and pattern recognition systems. We have proposed an automated vision system for follicles detection based on the classification of digitized microscopic scalp images using an ensemble of convolutional neural networks (CNN). A pool of adapted state-of-the-art CNNs have been transfer-trained on over 700k microscopic skin image regions of 120x120 pixels and their outputs further fed to the final stacked ensemble learning layer to capture a wider context of the connected neighboring regions of the original FullHD scalp images. A high validated f1 score (0.7) of detecting regions with follicles beats the industry's benchmark and brings this technology a step closer towards automated hair treatment as well as other emerging applications such as personal identification based on follicular scalp map.

关键词： pattern recognition Convolutional neural networks Stacked ensemble learning

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Pulsar: Efficient Sphere-based Neural Rendering

Pulsar: Efficient Sphere-based Neural Rendering

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

作者： Lassner, Christoph Zollhofer, Michael Facebook Real Labs Redmond WA 98052 USA

ISBN: (纸本)9781665445092

We propose Pulsar, an efficient sphere-based differentiable rendering module that is orders of magnitude faster than competing techniques, modular, and easy-to-use due to its tight integration with PyTorch. Differentiable rendering is the foundation for modern neural rendering approaches, since it enables end-to-end training of 3D scene representations from image observations. However, gradient-based optimization of neural mesh, voxel, or function representations suffers from multiple challenges, i.e., topological inconsistencies, high memory footprints, or slow rendering speeds. To alleviate these problems, Pulsar employs: 1) a sphere-based scene representation, 2) a modular, efficient differentiable projection operation, and 3) (optional) neural shading. Pulsar executes orders of magnitude faster than existing techniques and allows real-time rendering and optimization of representations with millions of spheres. Using spheres for the scene representation, unprecedented speed is obtained while avoiding topology problems. Pulsar is fully differentiable and thus enables a plethora of applications, ranging from 3D reconstruction to neural rendering.

关键词： Training Three-dimensional displays Scalability Rendering (computer graphics) Real-time systems Distance measurement Topology

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Can audio-visual integration strengthen robustness under multimodal attacks?

Can audio-visual integration strengthen robustness under mul...

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

作者： Tian, Yapeng Xu, Chenliang Univ Rochester Rochester NY 14627 USA

ISBN: (纸本)9781665445092

In this paper, we propose to make a systematic study on machines' multisensory perception under attacks. We use the audio-visual event recognition task against multimodal adversarial attacks as a proxy to investigate the robustness of audio-visual learning. We attack audio, visual, and both modalities to explore whether audio-visual integration still strengthens perception and how different fusion mechanisms affect the robustness of audio-visual models. For interpreting the multimodal interactions under attacks, we learn a weakly-supervised sound source visual localization model to localize sounding regions in videos. To mitigate multimodal attacks, we propose an audio-visual defense approach based on an audio-visual dissimilarity constraint and external feature memory banks. Extensive experiments demonstrate that audio-visual models are susceptible to multimodal adversarial attacks;audio-visual integration could decrease the model robustness rather than strengthen under multimodal attacks;even a weaklysupervised sound source visual localization model can be successfully fooled;our defense method can improve the invulnerability of audio-visual networks without significantly sacrificing clean model performance. The source code and pre-trained models are released in https://***/YapengTian/AV-Robustness-CVPR21.

关键词： Location awareness Visualization computer vision Systematics Codes Computational modeling Robustness

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What If We Only Use Real Datasets for Scene Text recognition? Toward Scene Text recognition With Fewer Labels

What If We Only Use Real Datasets for Scene Text Recognition...

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

作者： Baek, Jeonghun Matsui, Yusuke Aizawa, Kiyoharu Univ Tokyo Tokyo Japan

ISBN: (纸本)9781665445092

Scene text recognition (STR) task has a common practice: All state-of-the-art STR models are trained on large synthetic data. In contrast to this practice, training STR models only on fewer real labels (STR with fewer labels) is important when we have to train STR models without synthetic data: for handwritten or artistic texts that are difficult to generate synthetically and for languages other than English for which we do not always have synthetic data. However, there has been implicit common knowledge that training STR models on real data is nearly impossible because real data is insufficient. We consider that this common knowledge has obstructed the study of STR with fewer labels. In this work, we would like to reactivate STR with fewer labels by disproving the common knowledge. We consolidate recently accumulated public real data and show that we can train STR models satisfactorily only with real labeled data. Subsequently, we find simple data augmentation to fully exploit real data. Furthermore, we improve the models by collecting unlabeled data and introducing semi- and self-supervised methods. As a result, we obtain a competitive model to state-of-the-art methods. To the best of our knowledge, this is the first study that 1) shows sufficient performance by only using real labels and 2) introduces semi- and self-supervised methods into STR with fewer labels.

关键词： Training computer vision Codes Text recognition Data models Task analysis

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Information Bottleneck Disentanglement for Identity Swapping

Information Bottleneck Disentanglement for Identity Swapping

引用

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

作者： Gao, Gege Huang, Huaibo Fu, Chaoyou Li, Zhaoyang He, Ran Univ Chinese Acad Sci Ctr Excellence Brain Sci & Intelligence Technol Sch Artificial Intelligence Natl Lab Pattern RecognitCASIACAS Beijing Peoples R China

ISBN: (纸本)9781665445092

Improving the performance of face forgery detectors often requires more identity-swapped images of higher-quality. One core objective of identity swapping is to generate identity-discriminative faces that are distinct from the target while identical to the source. To this end, properly disentangling identity and identity-irrelevant information is critical and remains a challenging endeavor. In this work, we propose a novel information disentangling and swapping network, called InfoSwap, to extract the most expressive information for identity representation from a pre-trained face recognition model. The key insight of our method is to formulate the learning of disentangled representations as optimizing an information bottleneck tradeoff, in terms of finding an optimal compression of the pretrained latent features. Moreover, a novel identity contrastive loss is proposed for further disentanglement by requiring a proper distance between the generated identity and the target. While the most prior works have focused on using various loss functions to implicitly guide the learning of representations, we demonstrate that our model can provide explicit supervision for learning disentangled representations, achieving impressive performance in generating more identity-discriminative swapped faces.

关键词： computer vision Face recognition Buildings Detectors Benchmark testing Feature extraction Forgery

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NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections

NeRF in the Wild: Neural Radiance Fields for Unconstrained P...

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

作者： Martin-Brualla, Ricardo Radwan, Noha Sajjadi, Mehdi S. M. Barron, Jonathan T. Dosovitskiy, Alexey Duckworth, Daniel Google Res Mountain View CA 94043 USA

ISBN: (纸本)9781665445092

We present a learning-based method for synthesizing novel views of complex scenes using only unstructured collections of in-the-wild photographs. We build on Neural Radiance Fields (NeRF), which uses the weights of a multi-layer perceptron to model the density and color of a scene as a function of 3D coordinates. While NeRF works well on images of static subjects captured under controlled settings, it is incapable of modeling many ubiquitous, real-world phenomena in uncontrolled images, such as variable illumination or transient occluders. We introduce a series of extensions to NeRF to address these issues, thereby enabling accurate reconstructions from unstructured image collections taken from the internet. We apply our system, dubbed NeRF-W, to internet photo collections of famous landmarks, and demonstrate temporally consistent novel view renderings that are significantly closer to photorealism than the prior state of the art.

关键词： Learning systems Solid modeling Photorealism Three-dimensional displays Image color analysis Lighting Rendering (computer graphics)

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Visual Semantic Role Labeling for Video Understanding

Visual Semantic Role Labeling for Video Understanding

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

作者： Sadhu, Arka Gupta, Tanmay Yatskar, Mark Nevatia, Ram Kembhavi, Aniruddha Univ Southern Calif Los Angeles CA 90007 USA Univ Penn Philadelphia PA 19104 USA PRIOR Allen Inst AI Seattle WA USA PRIOR AI2 Seattle WA USA

ISBN: (纸本)9781665445092

We propose a new framework for understanding and representing related salient events in a video using visual semantic role labeling. We represent videos as a set of related events, wherein each event consists of a verb and multiple entities that fulfill various roles relevant to that event. To study the challenging task of semantic role labeling in videos or VidSRL, we introduce the VidSitu benchmark, a large scale video understanding data source with 29K 10-second movie clips richly annotated with a verb and semantic-roles every 2 seconds. Entities are co-referenced across events within a movie clip and events are connected to each other via event-event relations. Clips in VidSitu are drawn from a large collection of movies (similar to 3K) and have been chosen to be both complex (similar to 4.2 unique verbs within a video) as well as diverse (similar to 200 verbs have more than 100 annotations each). We provide a comprehensive analysis of the dataset in comparison to other publicly available video understanding benchmarks, several illustrative baselines and evaluate a range of standard video recognition models. Our code and dataset is available at ***.

关键词： Visualization computer vision Annotations Semantics Benchmark testing Motion pictures pattern recognition

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Counterfactual VQA: A Cause-Effect Look at Language Bias

Counterfactual VQA: A Cause-Effect Look at Language Bias

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

作者： Niu, Yulei Tang, Kaihua Zhang, Hanwang Lu, Zhiwu Hua, Xian-Sheng Wen, Ji-Rong Nanyang Technol Univ Singapore Singapore Renmin Univ China Gaoling Sch Artificial Intelligence Beijing Peoples R China Beijing Key Lab Big Data Management & Anal Method Beijing Peoples R China Alibaba Grp Damo Acad Hangzhou Peoples R China

ISBN: (纸本)9781665445092

VQA models may tend to rely on language bias as a shortcut and thus fail to sufficiently learn the multi-modal knowledge from both vision and language. Recent debiasing methods proposed to exclude the language prior during inference. However, they fail to disentangle the "good" language context and "bad" language bias from the whole. In this paper, we investigate how to mitigate language bias in VQA. Motivated by causal effects, we proposed a novel counterfactual inference framework, which enables us to capture the language bias as the direct causal effect of questions on answers and reduce the language bias by subtracting the direct language effect from the total causal effect. Experiments demonstrate that our proposed counterfactual inference framework 1) is general to various VQA backbones and fusion strategies, 2) achieves competitive performance on the language-bias sensitive VQA-CP dataset while performs robustly on the balanced VQA v2 dataset without any augmented data.

关键词： computer vision Codes Linguistics Robustness Cognition pattern recognition

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Mask Guided Matting via Progressive Refinement Network

Mask Guided Matting via Progressive Refinement Network

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

作者： Yu, Qihang Zhang, Jianming Zhang, He Wang, Yilin Lin, Zhe Xu, Ning Bai, Yutong Yuille, Alan Johns Hopkins Univ Baltimore MD 21218 USA Adobe San Jose CA USA

ISBN: (纸本)9781665445092

We propose Mask Guided (MG) Matting, a robust matting framework that takes a general coarse mask as guidance. MG Matting leverages a network (PRN) design which encourages the matting model to provide self-guidance to progressively refine the uncertain regions through the decoding process. A series of guidance mask perturbation operations are also introduced in the training to further enhance its robustness to external guidance. We show that PRN can generalize to unseen types of guidance masks such as trimap and low-quality alpha matte, making it suitable for various application pipelines. In addition, we revisit the foreground color prediction problem for matting and propose a surprisingly simple improvement to address the dataset issue. Evaluation on real and synthetic benchmarks shows that MG Matting achieves state-of-the-art performance using various types of guidance inputs.

关键词： Training Image segmentation Image color analysis Computational modeling Perturbation methods Pipelines Rendering (computer graphics)

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