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Cited by 395

The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.

Learning to See by Moving

Agrawal, Pulkit, Carreira, Joao, Malik, Jitendra·2015·2015 IEEE International Conference on Computer Vision (ICCV)
DOI 10.1109/iccv.2015.13
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Abstract

The current dominant paradigm for feature learning in computer vision relies on training neural networks for the task of object recognition using millions of hand labelled images. Is it also possible to learn features for a diverse set of visual tasks using any other form of supervision? In biology, living organisms developed the ability of visual perception for the purpose of moving and acting in the world. Drawing inspiration from this observation, in this work we investigated if the awareness of egomotion(i.e. self motion) can be used as a supervisory signal for feature learning. As opposed to the knowledge of class labels, information about egomotion is freely available to mobile agents. We found that using the same number of training images, features learnt using egomotion as supervision compare favourably to features learnt using class-label as supervision on the tasks of scene recognition, object recognition, visual odometry and keypoint matching.

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Cites

Show all 14 references

Research works printed but not linked: 22

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Cited by

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Metadata

Authors
Agrawal, P., Carreira, J., & Malik, J.
Venue
2015 IEEE International Conference on Computer Vision (ICCV)
Publisher
IEEE
Published
2015-12
Pages
37-45
DOI
10.1109/iccv.2015.13
OpenAlex
W1520997877
Cited by
395
References
14
FWCI
17.60
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: Learning to See by Moving. https://app.cochat.ai/papers/10.1109/iccv.2015.13
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Agrawal, P., Carreira, J., & Malik, J. (2015). Learning to See by Moving. 2015 IEEE International Conference on Computer Vision (ICCV). https://doi.org/10.1109/iccv.2015.13

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