TensorMask: A Foundation for Dense Object Segmentation Article Swipe

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Xinlei Chen , Ross Girshick , Kaiming He , Piotr Dollár ·

YOU? · · 2019 · Open Access · · DOI: https://doi.org/10.48550/arxiv.1903.12174

Sliding-window object detectors that generate bounding-box object predictions over a dense, regular grid have advanced rapidly and proven popular. In contrast, modern instance segmentation approaches are dominated by methods that first detect object bounding boxes, and then crop and segment these regions, as popularized by Mask R-CNN. In this work, we investigate the paradigm of dense sliding-window instance segmentation, which is surprisingly under-explored. Our core observation is that this task is fundamentally different than other dense prediction tasks such as semantic segmentation or bounding-box object detection, as the output at every spatial location is itself a geometric structure with its own spatial dimensions. To formalize this, we treat dense instance segmentation as a prediction task over 4D tensors and present a general framework called TensorMask that explicitly captures this geometry and enables novel operators on 4D tensors. We demonstrate that the tensor view leads to large gains over baselines that ignore this structure, and leads to results comparable to Mask R-CNN. These promising results suggest that TensorMask can serve as a foundation for novel advances in dense mask prediction and a more complete understanding of the task. Code will be made available.

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Segmentation Fault

Computer Science

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Segmentation Minimum bounding box Bounding overwatch Computer science Object (grammar) Task (project management) Grid Artificial intelligence Sliding window protocol Tensor (intrinsic definition) Object detection Pattern recognition (psychology) Window (computing) Computer vision Algorithm Image (mathematics) Mathematics Geometry Management Economics Operating system

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/1903.12174
PDF: https://arxiv.org/pdf/1903.12174
OA Status: green
Cited By: 36
References: 37
Related Works: 10
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DOI: https://doi.org/10.48550/arxiv.1903.12174

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Title: TensorMask: A Foundation for Dense Object Segmentation

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Type: preprint

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Language: en

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Publication year: 2019

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Publication date: 2019-03-28

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Authors: Xinlei Chen, Ross Girshick, Kaiming He, Piotr Dollár

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Landing page: https://arxiv.org/abs/1903.12174

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Concepts: Segmentation, Minimum bounding box, Bounding overwatch, Computer science, Object (grammar), Task (project management), Grid, Artificial intelligence, Sliding window protocol, Tensor (intrinsic definition), Object detection, Pattern recognition (psychology), Window (computing), Computer vision, Algorithm, Image (mathematics), Mathematics, Geometry, Management, Economics, Operating system

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Cited by: 36

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abstract_inverted_index.this,	105
abstract_inverted_index.treat	107
abstract_inverted_index.which	59
abstract_inverted_index.work,	49
abstract_inverted_index.R-CNN.	46, 160
abstract_inverted_index.boxes,	34
abstract_inverted_index.called	123
abstract_inverted_index.dense,	10
abstract_inverted_index.detect	31
abstract_inverted_index.ignore	150
abstract_inverted_index.itself	94
abstract_inverted_index.modern	21
abstract_inverted_index.object	1, 6, 32, 84
abstract_inverted_index.output	88
abstract_inverted_index.proven	17
abstract_inverted_index.tensor	141
abstract_inverted_index.enables	131
abstract_inverted_index.general	121
abstract_inverted_index.methods	28
abstract_inverted_index.present	119
abstract_inverted_index.rapidly	15
abstract_inverted_index.regular	11
abstract_inverted_index.results	156, 163
abstract_inverted_index.segment	39
abstract_inverted_index.spatial	91, 101
abstract_inverted_index.suggest	164
abstract_inverted_index.tensors	117
abstract_inverted_index.advanced	14
abstract_inverted_index.advances	174
abstract_inverted_index.bounding	33
abstract_inverted_index.captures	127
abstract_inverted_index.complete	182
abstract_inverted_index.generate	4
abstract_inverted_index.geometry	129
abstract_inverted_index.instance	22, 57, 109
abstract_inverted_index.location	92
abstract_inverted_index.paradigm	53
abstract_inverted_index.popular.	18
abstract_inverted_index.regions,	41
abstract_inverted_index.semantic	80
abstract_inverted_index.tensors.	136
abstract_inverted_index.baselines	148
abstract_inverted_index.contrast,	20
abstract_inverted_index.detectors	2
abstract_inverted_index.different	72
abstract_inverted_index.dominated	26
abstract_inverted_index.formalize	104
abstract_inverted_index.framework	122
abstract_inverted_index.geometric	96
abstract_inverted_index.operators	133
abstract_inverted_index.promising	162
abstract_inverted_index.structure	97
abstract_inverted_index.TensorMask	124, 166
abstract_inverted_index.approaches	24
abstract_inverted_index.available.	191
abstract_inverted_index.comparable	157
abstract_inverted_index.detection,	85
abstract_inverted_index.explicitly	126
abstract_inverted_index.foundation	171
abstract_inverted_index.prediction	76, 113, 178
abstract_inverted_index.structure,	152
abstract_inverted_index.demonstrate	138
abstract_inverted_index.dimensions.	102
abstract_inverted_index.investigate	51
abstract_inverted_index.observation	65
abstract_inverted_index.popularized	43
abstract_inverted_index.predictions	7
abstract_inverted_index.bounding-box	5, 83
abstract_inverted_index.segmentation	23, 81, 110
abstract_inverted_index.surprisingly	61
abstract_inverted_index.fundamentally	71
abstract_inverted_index.segmentation,	58
abstract_inverted_index.understanding	183
abstract_inverted_index.Sliding-window	0
abstract_inverted_index.sliding-window	56
abstract_inverted_index.under-explored.	62
cited_by_percentile_year
countries_distinct_count	0
institutions_distinct_count	4
citation_normalized_percentile