A joint end-to-end learning method for multi-view object instance detection and re-identification that incorporates learned geometric soft constraints, validated on a new street-level panorama dataset.
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2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2019 2verdicts
UNVERDICTED 2representative citing papers
Classical agents outperform learning-based ones on MINOS and Stanford 3D Indoor Spaces, with learned agents weaker at collision avoidance and memory but stronger at handling ambiguity and noise.
citing papers explorer
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Simultaneous multi-view instance detection with learned geometric soft-constraints
A joint end-to-end learning method for multi-view object instance detection and re-identification that incorporates learned geometric soft constraints, validated on a new street-level panorama dataset.
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To Learn or Not to Learn: Analyzing the Role of Learning for Navigation in Virtual Environments
Classical agents outperform learning-based ones on MINOS and Stanford 3D Indoor Spaces, with learned agents weaker at collision avoidance and memory but stronger at handling ambiguity and noise.