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感知人工智能前沿:第一人称视频和多模态感知.pdf

上传人: orig****ity 编号:185115 2024-08-05 62页 8.96MB

1、Frontiers in Perceptual AI:First-Person Video and Multimodal PerceptionKristen GraumanUniversity of Texas at AustinFAIR,Meta AIThe third-person Web perceptual experienceCaltech 101(2004),Caltech 256(2006)PASCAL(2007-12)ImageNet(2009)LabelMe(2007)MS COCO(2014)SUN(2010)Places(2014)BSD(2001)Visual Geno

2、me(2016)AVA(2018)Kinetics(2017)ActivityNet(2015)A curated“disembodied”moment in time from a spectators perspectiveKristen Grauman,FAIR&UT AustinFirst-person“egocentric”perceptual experienceUncurated long-form video stream driven by the agents goals,interactions,and attentionKristen Grauman,FAIR&UT A

3、ustinFirst-person perception and learningStatus quo:Learning and inference with“disembodied”images/videos.On the horizon:Visual learning in the context of agent goals,interaction,andmulti-sensory observations.Kristen Grauman,FAIR&UT AustinWhy egocentric video?Robot learningAugmented realityKristen G

4、rauman,FAIR&UT AustinExisting first-person video datasetsInspire our effort,but call for greater scale,content,diversityEPIC Kitchens Damen et al.202045 people,100 hrskitchens onlyUT EgoLee et al.20124 people,17 hrsdaily life,in/outdoorsEGTEA Gaze+Li et al.201832 people,28 hrskitchens onlyCharades-E

5、goSigurdsson 201871 people,34 hrsindoorADLPirsiavash 201220 people,10 hrsapartmentKristen Grauman,FAIR&UT AustinExisting first-person video datasetsInspire our effort,but call for greater scale,content,diversityEPIC Kitchens Damen et al.202045 people,100 hrskitchens onlyUT EgoLee et al.20124 people,

6、17 hrsdaily life,in/outdoorsEGTEA Gaze+Li et al.201832 people,28 hrskitchens onlyCharades-EgoSigurdsson 201871 people,34 hrsindoorADLPirsiavash 201220 people,10 hrsapartment#people#hours#scenesEgo4D GOALKristen Grauman,FAIR&UT Austin#Hours#ParticipantsEPIC-Kitchens-100Combining all prior egocentric

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本文主要介绍了UT Austin和FAIR联合推出的Ego4D dataset,这是一个大规模的多模态第一人称视角数据集,用于推动机器视觉和听觉学习的发展。Ego4D包含了3D环境扫描、多摄像头、眼动追踪以及音频等多种数据类型,覆盖了日常生活、工作、娱乐等场景,总时长超过3670小时,参与人数达931人。数据集通过隐私和伦理审查,确保了数据收集和使用的合规性。Ego4D benchmark suite包括了诸如Episodic Memory、Forecasting、Hands & Objects等任务,旨在促进研究社区的发展。此外,文章还提出了一种层级化的视频-语言嵌入学习方法,以及利用回声定位恢复场景形状的方法。通过这些方法,机器学习模型能够在未知环境中进行更准确的预测和导航。
如何改变机器人学习与感知?" 如何在未知环境中实现自我导航?" 如何通过语音分离提升人工智能?"
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