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APES: Audiovisual Person Search in Untrimmed Video

APES: Audiovisual Person Search in Untrimmed Video

引用

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

作者： Alcazar, Juan Leon Heilbron, Fabian Caba Mai, Long Perazzi, Federico Lee, Joon-Young Arbelaez, Pablo Ghanem, Bernard Univ Los Andes Bogota Colombia Adobe Res San Jose CA USA King Abdullah Univ Sci & Technol Thuwal Saudi Arabia

ISBN: (纸本)9781665448994

Humans are arguably one of the most important subjects in video streams, many real-world applications such as video summarization or video editing workflows often require the automatic search and retrieval of a person of interest. Despite tremendous efforts in the person re-identification and retrieval domains, few works have developed audiovisual search strategies. In this paper, we present the Audiovisual Person Search dataset (APES), a new dataset composed of untrimmed videos whose audio (voices) and visual (faces) streams are densely annotated. APES contains over 1.9K identities labeled along 36 hours of video, making it the largest dataset available for untrimmed audiovisual person search. A key property of APES is that it includes dense temporal annotations that link faces to speech segments of the same identity. To showcase the potential of our new dataset, we propose an audiovisual baseline and benchmark for person retrieval. Our study shows that modeling audiovisual cues benefits the recognition of people's identities.

关键词： Visualization computer vision Annotations conferences Streaming media Benchmark testing Search problems

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Inaccuracy of State-Action Value Function For Non-Optimal Actions in Adversarially Trained Deep Neural Policies

Inaccuracy of State-Action Value Function For Non-Optimal Ac...

引用

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

作者： Korkmaz, Ezgi KTH Royal Inst Technol Stockholm Sweden

ISBN: (纸本)9781665448994

The introduction of deep neural networks as function approximator for the state-action value function has led to the creation of a new research area for self-learning systems that explore policies from high dimensional input. While the success of deep neural policies has resulted in the deployment of these policies in diversified application domains, there are significant concerns regarding their robustness towards specifically crafted malicious perturbations introduced to their inputs. Several studies have focused on making deep neural policies resistant to such perturbations via training with the existence of these perturbations (i.e. adversarial training). In this paper we focus on conducting an investigation on the state-action value function learned by state-of-the-art adversarially trained deep neural policies and vanilla trained deep neural policies. We perform several experiments in the OpenAI Baselines and we show that the state-action value functions learned by vanilla trained deep neural policies have better estimates for the non-optimal actions than the state-of-the-art adversarially trained deep neural policies. We believe our study lays out intriguing properties of adversarial training and could be critical step towards obtaining robust and reliable policies.

关键词： Training Resistance Deep learning computer vision Systematics Perturbation methods conferences

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SrvfNet: A Generative Network for Unsupervised Multiple Diffeomorphic Functional Alignment

SrvfNet: A Generative Network for Unsupervised Multiple Diff...

引用

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

作者： Nunez, Elvis Lizarraga, Andrew Joshi, Shantanu H. Univ Calif Los Angeles Dept Elect & Comp Engn Los Angeles CA 90024 USA Univ Calif Los Angeles Dept Bioengn Los Angeles CA 90024 USA Univ Calif Los Angeles Dept Neurol Ahmanson Lovelace Brain Mapping Ctr Los Angeles CA 90024 USA

ISBN: (纸本)9781665448994

We present SrvfNet, a generative deep learning framework for the joint multiple alignment of large collections of functional data comprising square-root velocity functions (SRVF) to their templates. Our proposed framework is fully unsupervised and is capable of aligning to a predefined template as well as jointly predicting an optimal template from data while simultaneously achieving alignment. Our network is constructed as a generative encoder-decoder architecture comprising fully-connected layers capable of producing a distribution space of the warping functions. We demonstrate the strength of our framework by validating it on synthetic data as well as diffusion profiles from magnetic resonance imaging (MRI) data.

关键词： Deep learning computer vision Magnetic resonance imaging conferences Computational modeling Training data computer architecture

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GOO: A Dataset for Gaze Object Prediction in Retail Environments

GOO: A Dataset for Gaze Object Prediction in Retail Environm...

引用

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

作者： Tomas, Henri Reyes, Marcus Dionido, Raimarc Ty, Mark Mirando, Jonric Casimiro, Joel Atienza, Rowel Guinto, Richard Univ Philippines Quezon City Philippines Samsung R&D Inst Philippines Taguig Philippines

ISBN: (纸本)9781665448994

One of the most fundamental and information-laden actions humans do is to look at objects. However, a survey of current works reveals that existing gaze-related datasets annotate only the pixel being looked at, and not the boundaries of a specific object of interest. This lack of object annotation presents an opportunity for further advancing gaze estimation research. To this end, we present a challenging new task called gaze object prediction, where the goal is to predict a bounding box for a person's gazed-at object. To train and evaluate gaze networks on this task, we present the Gaze On Objects (GOO) dataset. GOO is composed of a large set of synthetic images (GOO-Synth) supplemented by a smaller subset of real images (GOO-Real) of people looking at objects in a retail environment. Our work establishes extensive baselines on GOO by re-implementing and evaluating selected state-of-the-art models on the task of gaze following and domain adaptation. Code is available(1) on github.

关键词： Training computer vision Adaptation models conferences Estimation computer architecture Benchmark testing

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Low Bandwidth Video-Chat Compression using Deep Generative Models

Low Bandwidth Video-Chat Compression using Deep Generative M...

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

作者： Oquab, Maxime Stock, Pierre Gafni, Oran Haziza, Daniel Xu, Tao Zhang, Peizhao Celebi, Onur Hasson, Yana Labatut, Patrick Bose-Kolanu, Bobo Peyronel, Thibault Couprie, Camille Facebook Menlo Pk CA 94025 USA INRIA Le Chesnay France

ISBN: (纸本)9781665448994

To unlock video chat for hundreds of millions of people hindered by poor connectivity or unaffordable data costs, we propose to authentically reconstruct faces on the receiver's device using facial landmarks extracted at the sender's side and transmitted over the network. In this context, we discuss and evaluate the benefits and disadvantages of several deep adversarial approaches. In particular, we explore quality and bandwidth trade-offs for approaches based on static landmarks, dynamic landmarks or segmentation maps. We design a mobile-compatible architecture based on the first order animation model of Siarohin et al. In addition, we leverage SPADE blocks to refine results in important areas such as the eyes and lips. We compress the networks down to about 3 MB, allowing models to run in real time on iPhone 8 (CPU). This approach enables video calling at a few kbits per second, an order of magnitude lower than currently available alternatives.

关键词： Image segmentation Image coding Lips Bandwidth computer architecture Video compression Streaming media

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NTIRE 2023 Challenge on Image Denoising: Methods and Results

NTIRE 2023 Challenge on Image Denoising: Methods and Results

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Li, Yawei Zhang, Yulun Van Gool, Luc Timofte, Radu Tu, Zhijun Du, Kunpeng Wang, Hailing Chen, Hanting Li, Wei Wang, Xiaofei Hu, Jie Wang, Yunhe Kong, Xiangyu Wu, Jinlong Zhang, Dafeng Zhang, Jianxing Liu, Shuai Bai, Furui Feng, Chaoyu Wang, Hao Zhang, Yuqian Shao, Guangqi Wang, Xiaotao Lei, Lei Xu, Rongjian Zhang, Zhilu Chen, Yunjin Ren, Dongwei Zuo, Wangmeng Wu, Qi Han, Mingyan Cheng, Shen Li, Haipeng Jiang, Ting Jiang, Chengzhi Li, Xinpeng Luo, Jinting Lin, Wenjie Yu, Lei Fan, Haoqiang Liu, Shuaicheng Arora, Aditya Zamir, Syed Waqas Vazquez-Corral, Javier Derpanis, Konstantinos G. Brown, Michael S. Li, Hao Zhao, Zhihao Pan, Jinshan Dong, Jiangxin Tang, Jinhui Yang, Bo Chen, Jingxiang Li, Chenghua Zhang, Xi Zhang, Zhao Ren, Jiahuan Ji, Zhicheng Miao, Kang Zhao, Suiyi Zheng, Huan Wei, YanYan Liu, Kangliang Du, Xiangcheng Liu, Sijie Zheng, Yingbin Wu, Xingjiao Jin, Cheng Wang, Yan Chen, Jiayin Wu, Xiaoxuan Chen, Huiming Zheng, Xing Chen, Yejia Irny, Rajeev Koundinya, Sriharsha Kamath, Vighnesh Khandelwal, Gaurav Khowaja, Sunder Ali Yoon, Jiseok Lee, Ik Hyun Chen, Shijie Zhao, Chengqiang Yang, Huabin Zhang, Zhongjian Huang, Junjia Zhang, Yanru Computer Vision Lab D-ITET Eth Zürich Switzerland Computer Vision Lab Ifi & Caidas University of Würzburg Germany Huawei Noah'Ark Lab Canada Huawei Consumer Business Group China Xiaomi Inc. China Harbin Institute of Technology China Megvii Technology China York University Toronto Canada Abu Dhabi United Arab Emirates Computer Vision Center Universitat Autonòma de Barcelona Spain Nanjing University of Science and Technology China Nanjing University of Information Science and Technology China China Fudan University China Videt Technology South China University of Technology China India University of Sindh Pakistan Iklab Inc. Korea Republic of Iklab Inc. Tech University of Korea Korea Republic of Xuzhou Medical University China Southwest Jiaotong University China

ISBN: (纸本)9798350302493

This paper reviews the NTIRE 2023 challenge on image denoising (σ = 50) with a focus on the proposed solutions and results. The aim is to obtain a network design capable to produce high-quality results with the best performance measured by PSNR for image denoising. Independent additive white Gaussian noise (AWGN) is assumed and the noise level is 50. The challenge had 225 registered participants, and 16 teams made valid submissions. They gauge the state-of-the-art for image denoising. © 2023 ieee.

关键词： Image denoising

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Comparing Representations in Tracking for Event Camera-based SLAM

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

引用

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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Supervised deep learning of elastic SRV distances on the shape space of curves

Supervised deep learning of elastic SRV distances on the sha...

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

作者： Hartman, Emmanuel Sukurdeep, Yashil Charon, Nicolas Klassen, Eric Bauer, Martin Florida State Univ Dept Math Tallahassee FL 32306 USA Johns Hopkins Univ Dept Appl Math Baltimore MD USA

ISBN: (纸本)9781665448994

Motivated by applications from computer vision to bioinformatics, the field of shape analysis deals with problems where one wants to analyze geometric objects, such as curves, while ignoring actions that preserve their shape, such as translations, rotations, scalings, or reparametrizations. Mathematical tools have been developed to define notions of distances, averages, and optimal deformations for geometric objects. One such framework, which has proven to be successful in many applications, is based on the square root velocity (SRV) transform, which allows one to define a computable distance between spatial curves regardless of how they are parametrized. This paper introduces a supervised deep learning framework for the direct computation of SRV distances between curves, which usually requires an optimization over the group of reparametrizations that act on the curves. The benefits of our approach in terms of computational speed and accuracy are illustrated via several numerical experiments on both synthetic and real data.

关键词： Training Deep learning computer vision Shape conferences Transforms Tools

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Unsupervised Image-to-Image Translation with Generative Prior

Unsupervised Image-to-Image Translation with Generative Prio...

引用

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

作者： Yang, Shuai Jiang, Liming Liu, Ziwei Loy, Chen Change Nanyang Technol Univ S Lab Singapore Singapore

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Unsupervised image-to-image translation aims to learn the translation between two visual domains without paired data. Despite the recent progress in image translation models, it remains challenging to build mappings between complex domains with drastic visual discrepancies. In this work, we present a novel framework, Generative Prior-guided UNsupervised Image-to-image Translation (GP-UNIT), to improve the overall quality and applicability of the translation algorithm. Our key insight is to leverage the generative prior from pre-trained class-conditional GANs (e.g., BigGAN) to learn rich content correspondences across various domains. We propose a novel coarse-to-fine scheme: we first distill the generative prior to capture a robust coarse-level content representation that can link objects at an abstract semantic level, based on which fine-level content features are adaptively learned for more accurate multi-level content correspondences. Extensive experiments demonstrate the superiority of our versatile framework over state-of-the-art methods in robust, high-quality and diversified translations, even for challenging and distant domains.

关键词： Visualization computer vision Codes Semantics pattern recognition

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ADNet: Attention-guided Deformable Convolutional Network for High Dynamic Range Imaging

ADNet: Attention-guided Deformable Convolutional Network for...

引用

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

作者： Liu, Zhen Lin, Wenjie Li, Xinpeng Rao, Qing Jiang, Ting Han, Mingyan Fan, Haoqiang Sun, Jian Liu, Shuaicheng Megvii Technol Beijing Peoples R China Sichuan Univ Chengdu Peoples R China Univ Elect Sci & Technol China Chengdu Peoples R China

ISBN: (纸本)9781665448994

In this paper, we present an attention-guided deformable convolutional network for hand-held multi frame high dynamic range (HDR) imaging, namely ADNet. This problem comprises two intractable challenges of how to handle saturation and noise properly and how to tackle misalignments caused by object motion or camera jittering. To address the former, we adopt a spatial attention module to adaptively select the most appropriate regions of various exposure low dynamic range (LDR) images for fusion. For the latter one, we propose to align the gamma-corrected images in the feature-level with a Pyramid, Cascading and Deformable (PCD) alignment module. The proposed ADNet shows state-of-the-art performance compared with previous methods, achieving a PSNR-l of 39.4471 and a PSNR-mu of 37.6359 in NTIRE 2021 Multi-Frame HDR Challenge.

关键词： Convolutional codes computer vision conferences Pipelines Dynamic range Cameras pattern recognition

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