Revisiting the Loss Weight Adjustment in Object Detection Article Swipe
Wenxin Yu
,
Xueling Shen
,
Jiajie Hu
,
Dong Yin
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2103.09488
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2103.09488
Object detection is a typical multi-task learning application, which optimizes classification and regression simultaneously. However, classification loss always dominates the multi-task loss in anchor-based methods, hampering the consistent and balanced optimization of the tasks. In this paper, we find that shifting the bounding boxes can change the division of positive and negative samples in classification, meaning classification depends on regression. Moreover, we summarize three important conclusions about fine-tuning loss weights, considering different datasets, optimizers and regression loss functions. Based on the above conclusions, we propose Adaptive Loss Weight Adjustment(ALWA) to solve the imbalance in optimizing anchor-based methods according to statistical characteristics of losses. By incorporating ALWA into previous state-of-the-art detectors, we achieve a significant performance gain on PASCAL VOC and MS COCO, even with L1, SmoothL1 and CIoU loss. The code is available at https://github.com/ywx-hub/ALWA.
Related Topics
Concepts
Pascal (unit)
Computer science
Bounding overwatch
Regression
Minimum bounding box
Task (project management)
Code (set theory)
Artificial intelligence
Object detection
Object (grammar)
Pattern recognition (psychology)
Machine learning
Statistics
Image (mathematics)
Mathematics
Engineering
Set (abstract data type)
Systems engineering
Programming language
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2103.09488
- https://arxiv.org/pdf/2103.09488
- OA Status
- green
- References
- 49
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3138746769
All OpenAlex metadata
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https://openalex.org/W3138746769Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2103.09488Digital Object Identifier
- Title
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Revisiting the Loss Weight Adjustment in Object DetectionWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-03-17Full publication date if available
- Authors
-
Wenxin Yu, Xueling Shen, Jiajie Hu, Dong YinList of authors in order
- Landing page
-
https://arxiv.org/abs/2103.09488Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2103.09488Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2103.09488Direct OA link when available
- Concepts
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Pascal (unit), Computer science, Bounding overwatch, Regression, Minimum bounding box, Task (project management), Code (set theory), Artificial intelligence, Object detection, Object (grammar), Pattern recognition (psychology), Machine learning, Statistics, Image (mathematics), Mathematics, Engineering, Set (abstract data type), Systems engineering, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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49Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.into | 106 |
| abstract_inverted_index.loss | 16, 21, 68, 76 |
| abstract_inverted_index.that | 39 |
| abstract_inverted_index.this | 35 |
| abstract_inverted_index.with | 123 |
| abstract_inverted_index.Based | 78 |
| abstract_inverted_index.COCO, | 121 |
| abstract_inverted_index.about | 66 |
| abstract_inverted_index.above | 81 |
| abstract_inverted_index.boxes | 43 |
| abstract_inverted_index.loss. | 128 |
| abstract_inverted_index.solve | 90 |
| abstract_inverted_index.three | 63 |
| abstract_inverted_index.which | 8 |
| abstract_inverted_index.Object | 0 |
| abstract_inverted_index.PASCAL | 117 |
| abstract_inverted_index.Weight | 87 |
| abstract_inverted_index.always | 17 |
| abstract_inverted_index.change | 45 |
| abstract_inverted_index.paper, | 36 |
| abstract_inverted_index.tasks. | 33 |
| abstract_inverted_index.achieve | 111 |
| abstract_inverted_index.depends | 57 |
| abstract_inverted_index.losses. | 102 |
| abstract_inverted_index.meaning | 55 |
| abstract_inverted_index.methods | 96 |
| abstract_inverted_index.propose | 84 |
| abstract_inverted_index.samples | 52 |
| abstract_inverted_index.typical | 4 |
| abstract_inverted_index.Adaptive | 85 |
| abstract_inverted_index.However, | 14 |
| abstract_inverted_index.SmoothL1 | 125 |
| abstract_inverted_index.balanced | 29 |
| abstract_inverted_index.bounding | 42 |
| abstract_inverted_index.division | 47 |
| abstract_inverted_index.learning | 6 |
| abstract_inverted_index.methods, | 24 |
| abstract_inverted_index.negative | 51 |
| abstract_inverted_index.positive | 49 |
| abstract_inverted_index.previous | 107 |
| abstract_inverted_index.shifting | 40 |
| abstract_inverted_index.weights, | 69 |
| abstract_inverted_index.Moreover, | 60 |
| abstract_inverted_index.according | 97 |
| abstract_inverted_index.available | 132 |
| abstract_inverted_index.datasets, | 72 |
| abstract_inverted_index.detection | 1 |
| abstract_inverted_index.different | 71 |
| abstract_inverted_index.dominates | 18 |
| abstract_inverted_index.hampering | 25 |
| abstract_inverted_index.imbalance | 92 |
| abstract_inverted_index.important | 64 |
| abstract_inverted_index.optimizes | 9 |
| abstract_inverted_index.summarize | 62 |
| abstract_inverted_index.consistent | 27 |
| abstract_inverted_index.detectors, | 109 |
| abstract_inverted_index.functions. | 77 |
| abstract_inverted_index.multi-task | 5, 20 |
| abstract_inverted_index.optimizers | 73 |
| abstract_inverted_index.optimizing | 94 |
| abstract_inverted_index.regression | 12, 75 |
| abstract_inverted_index.conclusions | 65 |
| abstract_inverted_index.considering | 70 |
| abstract_inverted_index.fine-tuning | 67 |
| abstract_inverted_index.performance | 114 |
| abstract_inverted_index.regression. | 59 |
| abstract_inverted_index.significant | 113 |
| abstract_inverted_index.statistical | 99 |
| abstract_inverted_index.anchor-based | 23, 95 |
| abstract_inverted_index.application, | 7 |
| abstract_inverted_index.conclusions, | 82 |
| abstract_inverted_index.optimization | 30 |
| abstract_inverted_index.incorporating | 104 |
| abstract_inverted_index.classification | 10, 15, 56 |
| abstract_inverted_index.characteristics | 100 |
| abstract_inverted_index.classification, | 54 |
| abstract_inverted_index.simultaneously. | 13 |
| abstract_inverted_index.Adjustment(ALWA) | 88 |
| abstract_inverted_index.state-of-the-art | 108 |
| abstract_inverted_index.https://github.com/ywx-hub/ALWA. | 134 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile |