Facing the void: Overcoming missing data in multi-view imagery
G Machado, MB Pereira, K Nogueira… - IEEE …, 2022 - ieeexplore.ieee.org
In some scenarios, a single input image may not be enough to allow the object classification.
In those cases, it is crucial to explore the complementary information extracted from images
presenting the same object from multiple perspectives (or views) in order to enhance the
general scene understanding and, consequently, increase the performance. However, this
task, commonly called multi-view image classification, has a major challenge: missing data.
In this paper, we propose a novel technique for multi-view image classification robust to this …
In those cases, it is crucial to explore the complementary information extracted from images
presenting the same object from multiple perspectives (or views) in order to enhance the
general scene understanding and, consequently, increase the performance. However, this
task, commonly called multi-view image classification, has a major challenge: missing data.
In this paper, we propose a novel technique for multi-view image classification robust to this …
[PDF][PDF] Facing the Void: Overcoming Missing Data in Multi-View Imagery
A DOS SANTOS - dspace.stir.ac.uk
In some scenarios, a single input image may not be enough to allow the object classification.
In those cases, it is crucial to explore the complementary information extracted from images
presenting the same object from multiple perspectives (or views) in order to enhance the
general scene understanding and, consequently, increase the performance. However, this
task, commonly called multi-view image classification, has a major challenge: missing data.
In this paper, we propose a novel technique for multi-view image classification robust to this …
In those cases, it is crucial to explore the complementary information extracted from images
presenting the same object from multiple perspectives (or views) in order to enhance the
general scene understanding and, consequently, increase the performance. However, this
task, commonly called multi-view image classification, has a major challenge: missing data.
In this paper, we propose a novel technique for multi-view image classification robust to this …
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