Deep Multicameral Decoding for Localizing Unoccluded Object Instances from a Single RGB Image
Occlusion-aware instance-sensitive segmentation is a complex task generally split into region-based segmentations, by approximating instances as their bounding box. We address the showcase scenario of dense homogeneous layouts in which this approximation does not hold. In this scenario, outlining un...
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| Vydáno v: | International journal of computer vision Ročník 128; číslo 5; s. 1331 - 1359 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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New York
Springer US
01.05.2020
Springer Springer Nature B.V Springer Verlag |
| Edice: | Special Issue on Deep Learning for Robotic Vision |
| Témata: | |
| ISSN: | 0920-5691, 1573-1405 |
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| Abstract | Occlusion-aware instance-sensitive segmentation is a complex task generally split into region-based segmentations, by approximating instances as their bounding box. We address the showcase scenario of dense homogeneous layouts in which this approximation does not hold. In this scenario, outlining unoccluded instances by decoding a deep encoder becomes difficult, due to the translation invariance of convolutional layers and the lack of complexity in the decoder. We therefore propose a multicameral design composed of subtask-specific lightweight decoder and encoder–decoder units, coupled in cascade to encourage subtask-specific feature reuse and enforce a learning path within the decoding process. Furthermore, the state-of-the-art datasets for occlusion-aware instance segmentation contain real images with few instances and occlusions mostly due to objects occluding the background, unlike dense object layouts. We thus also introduce a synthetic dataset of dense homogeneous object layouts, namely Mikado, which extensibly contains more instances and inter-instance occlusions per image than these public datasets. Our extensive experiments on Mikado and public datasets show that ordinal multiscale units within the decoding process prove more effective than state-of-the-art design patterns for capturing position-sensitive representations. We also show that Mikado is plausible with respect to real-world problems, in the sense that it enables the learning of performance-enhancing representations transferable to real images, while drastically reducing the need of hand-made annotations for finetuning. The proposed dataset will be made publicly available. |
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| AbstractList | Occlusion-aware instance-sensitive segmentation is a complex task generally split into region-based segmentations, by approximating instances as their bounding box. We address the showcase scenario of dense homogeneous layouts in which this approximation does not hold. In this scenario, outlining unoccluded instances by decoding a deep encoder becomes difficult, due to the translation invariance of convolutional layers and the lack of complexity in the decoder. We therefore propose a multicameral design composed of subtask-specific lightweight decoder and encoder–decoder units, coupled in cascade to encourage subtask-specific feature reuse and enforce a learning path within the decoding process. Furthermore, the state-of-the-art datasets for occlusion-aware instance segmentation contain real images with few instances and occlusions mostly due to objects occluding the background, unlike dense object layouts. We thus also introduce a synthetic dataset of dense homogeneous object layouts, namely Mikado, which extensibly contains more instances and inter-instance occlusions per image than these public datasets. Our extensive experiments on Mikado and public datasets show that ordinal multiscale units within the decoding process prove more effective than state-of-the-art design patterns for capturing position-sensitive representations. We also show that Mikado is plausible with respect to real-world problems, in the sense that it enables the learning of performance-enhancing representations transferable to real images, while drastically reducing the need of hand-made annotations for finetuning. The proposed dataset will be made publicly available. |
| Audience | Academic |
| Author | Dellandréa, Emmanuel Chen, Liming Grard, Matthieu |
| Author_xml | – sequence: 1 givenname: Matthieu surname: Grard fullname: Grard, Matthieu email: m.grard@sileane.com organization: Siléane, Université de Lyon, CNRS, École Centrale de Lyon LIRIS UMR5205 – sequence: 2 givenname: Emmanuel surname: Dellandréa fullname: Dellandréa, Emmanuel organization: Université de Lyon, CNRS, École Centrale de Lyon LIRIS UMR5205 – sequence: 3 givenname: Liming surname: Chen fullname: Chen, Liming organization: Université de Lyon, CNRS, École Centrale de Lyon LIRIS UMR5205 |
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| CitedBy_id | crossref_primary_10_1016_j_neucom_2022_04_023 crossref_primary_10_1155_2022_8367387 |
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| Keywords | Instance boundary and occlusion detection Domain adaptation Fully convolutional encoder–decoder networks Synthetic data Fully convolutional encoder-decoder networks |
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