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Learning to Disambiguate Strongly Interacting Hands via Probabilistic Per-pixel Part Segmentation
[article]
2021
arXiv
pre-print
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The method consists of two interwoven branches that process the input imagery into a per-pixel semantic part segmentation mask and a visual feature volume. ...
To do so, the part probabilities are merged with the visual features and processed via fully-convolutional layers. ...
While MJB is a part-time employee of Amazon, his research was performed solely at, and funded solely by, Max Planck. MJB has financial interests in Amazon, Datagen Technologies, and Meshcapade GmbH. ...
arXiv:2107.00434v2
fatcat:frrzchccbja4pclwkh4ebhposi
Learning To Disambiguate Strongly Interacting Hands via Probabilistic Per-Pixel Part Segmentation
2021
2021 International Conference on 3D Vision (3DV)
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When estimating the 3D pose of interacting hands, state-of-the-art methods struggle to disambiguate the appearance of the two hands and their parts. ...
Our model, DIGIT, reduces the ambiguity by predicting and leveraging a probabilistic part segmentation volume (2.1) to produce reliable pose estimates even when the two hands are in direct contact and ...
While MJB is a part-time employee of Amazon, his research was performed solely at, and funded solely by, Max Planck. MJB has financial interests in Amazon, Datagen Technologies, and Meshcapade GmbH. ...
doi:10.1109/3dv53792.2021.00011
fatcat:l6ozyteqv5dixa7x6vfgnponpq
Context based object categorization: A critical survey
2010
Computer Vision and Image Understanding
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Context information, based on the interaction among objects in the scene or global scene statistics, can help successfully disambiguate appearance inputs in recognition tasks. ...
The goal of object categorization is to locate and identify instances of an object category within an image. ...
Also, other machine learning models will be considered for a better integration of context features. ...
doi:10.1016/j.cviu.2010.02.004
fatcat:3ee2st4tffbnplewyrq5o4i5rm
Multi-class object localization by combining local contextual interactions
2010
2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
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Furthermore, we study context as (i) part of recognizing objects in images and (ii) as an advocate for label agreement to disambiguate object identity in recognition systems. ...
We analyze the contributions and trade -offs of integrating context and investigate contextual interactions between pixels, regions and objects in the scene. ...
Given an image I, its corresponding segments S 1 , . . . , S k , and probabilistic per-segment labels p(c i |S i ) (as in [65] ), we wish to find segment labels c 1 , . . . , c k ∈ C such that all agree ...
doi:10.1109/cvpr.2010.5540223
dblp:conf/cvpr/GalleguillosMBL10
fatcat:asm6h3hix5fufkipt56voasqcu
Learning to Regress Bodies from Images using Differentiable Semantic Rendering
[article]
2022
arXiv
pre-print
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2021 pre-print arXiv:2110.03480v1 fatcat:ye6mcq6jp5bmffygzc7uy6j6n4
Learning to regress 3D human body shape and pose (e.g. ...
~SMPL parameters) from monocular images typically exploits losses on 2D keypoints, silhouettes, and/or part-segmentation when 3D training data is not available. ...
On the other hand, relying too strongly on 3D priors introduces bias. To circumvent this problem, recent approaches [30, 34, 50] propose to use part-segmentations or silhouettes. ...
arXiv:2110.03480v2
fatcat:6oi25y5pw5bgdbhzunz47rh2ny
Processing reduced word-forms in speech perception using probabilistic knowledge about speech production
2009
Journal of Experimental Psychology: Human Perception and Performance
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Prior to disambiguation, listeners' fixations were drawn to /t/-final words more when boven than when naast followed the ambiguous sequences. ...
We thus argue that probabilistic knowledge about the effect of following context in speech production is used prelexically in perception to help resolve lexical ambiguities caused by continuous-speech ...
On the one hand, schwa deletion in a word such as president is likely to be independent of the word's segmental context. ...
doi:10.1037/a0012730
pmid:19170486
fatcat:crzskrhbqfhalpmbjerrhscvnm
Using Motion and Internal Supervision in Object Recognition
[article]
2018
arXiv
pre-print
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In the second and main part of the study we develop methods for using specific types of visual motion to solve two difficult problems in unsupervised visual learning: learning to recognize hands by their ...
We use our conclusions in this part to propose a model for several aspects of learning by human infants from their visual environment. ...
Thus, we can strongly believe that infants use motion as a major source of information for learning hands. ...
arXiv:1812.05455v1
fatcat:peih4lfnnjacncpvgeutjbk7ue
Pixel Objectness: Learning to Segment Generic Objects Automatically in Images and Videos
[article]
2018
arXiv
pre-print
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2018 pre-print arXiv:1808.04702v1 fatcat:tm4iynp52ncl3mvv2onjpac3ne
We propose an end-to-end learning framework for segmenting generic objects in both images and videos. ...
Thus we propose ways to exploit weakly labeled data for learning dense foreground segmentation. ...
ACKNOWLEDGEMENTS This research is supported in part by ONR YIP N00014-12-1-0754, an AWS Machine Learning Research Award, and DARPA Lifelong Learning Machines. ...
arXiv:1808.04702v2
fatcat:jvin6gvjwndehjcbz3n336e5f4
Pixel Objectness: Learning to Segment Generic Objects Automatically in Images and Videos
2018
IEEE Transactions on Pattern Analysis and Machine Intelligence
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We propose an end-to-end learning framework for segmenting generic objects in both images and videos. ...
Thus we propose ways to exploit weakly labeled data for learning dense foreground segmentation. ...
ACKNOWLEDGEMENTS This research is supported in part by ONR YIP N00014-12-1-0754, an AWS Machine Learning Research Award, and the DARPA Lifelong Learning Machines project. ...
doi:10.1109/tpami.2018.2865794
pmid:30130176
fatcat:nmx3kdvw7vcslfgb623lwc4ose
Fundamental Strategies for Solving Low-Level Vision Problems
2011
IPSJ Transactions on Computer Vision and Applications
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Solutions to low-level problems can be broadly group according to how they propagate local information to global representations. ...
Understanding these categorizations is useful because they offer guidance on how tools like machine learning can be implemented in these systems. ...
that the model is limited to the pairwise interactions. ...
doi:10.2197/ipsjtcva.3.95
fatcat:m6x6xitzhnbztmqhrskssdbf44
People Watching: Human Actions as a Cue for Single View Geometry
2014
International Journal of Computer Vision
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These constraints are then used to improve single-view 3D scene understanding approaches. ...
Our method builds upon recent advances in still-image pose estimation to extract functional and geometric constraints on the scene. ...
Acknowledgments: This work was supported by NSF Graduate Research and NDSEG Fellowships to DF, and by ONR-MURI N000141010934, NSF IIS-1320083, the MSR-INRIA laboratory, the EIT-ICT labs, Google, ERC Activia ...
doi:10.1007/s11263-014-0710-z
fatcat:auj4lihccvesnnl32wlq6zd34q
People Watching: Human Actions as a Cue for Single View Geometry
[chapter]
2012
Lecture Notes in Computer Science
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These constraints are then used to improve single-view 3D scene understanding approaches. ...
Our method builds upon recent advances in still-image pose estimation to extract functional and geometric constraints on the scene. ...
Acknowledgments: This work was supported by NSF Graduate Research and NDSEG Fellowships to DF, and by ONR-MURI N000141010934, NSF IIS-1320083, the MSR-INRIA laboratory, the EIT-ICT labs, Google, ERC Activia ...
doi:10.1007/978-3-642-33715-4_53
fatcat:ma4fdudovjf27fxvz7eo2alwhu
Context-Based Bayesian Intent Recognition
2012
IEEE Transactions on Autonomous Mental Development
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One of the foundations of social interaction among humans is the ability to correctly identify interactions and infer the intentions of others. ...
To build robots that reliably function in the human social world, we must develop models that robots can use to mimic the intent recognition skills found in humans. ...
Low-Level Recognition via Hidden Markov Models As mentioned above, our system uses HMMs to model activities that consist of a number of parts that have intentional significance. ...
doi:10.1109/tamd.2012.2211871
fatcat:kdw6kdirhbfxfk2wvq4ne7bgoe
Interleaved Object Categorization and Segmentation
2003
Procedings of the British Machine Vision Conference 2003
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These probabilities correspond to the per-pixel confidence the system has in its recognition and segmentation result. ...
In addition, it returns a per-pixel confidence estimate, specifying how much this segmentation can be trusted. ...
doi:10.5244/c.17.78
dblp:conf/bmvc/LeibeS03
fatcat:tdpwrxy44be73ctuxzdeyop26q
Unsupervised construction of human body models
2018
Cognitive Systems Research
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Unsupervised learning of a generalizable model of the visual appearance of humans from video data is of major importance for computing systems interacting naturally with their users and others. ...
The models from many videos are integrated into a meta-model, which shows good generalization with respect to different individuals, backgrounds, and attire. ...
Kumar et al. (2010) use their OCMs to guide a probabilistic foreground segmentation scheme that shows acceptable performance in cutting out members of the encoded categories from cluttered images. ...
doi:10.1016/j.cogsys.2017.08.001
fatcat:yfzw4dcddje5tjb4k2f6rny7hq
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