Overcoming Statistical Shortcuts for Open-ended Visual Counting Article Swipe
Corentin Dancette
,
Rémi Cadène
,
Xinlei Chen
,
Matthieu Cord
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2006.10079
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2006.10079
Machine learning models tend to over-rely on statistical shortcuts. These spurious correlations between parts of the input and the output labels does not hold in real-world settings. We target this issue on the recent open-ended visual counting task which is well suited to study statistical shortcuts. We aim to develop models that learn a proper mechanism of counting regardless of the output label. First, we propose the Modifying Count Distribution (MCD) protocol, which penalizes models that over-rely on statistical shortcuts. It is based on pairs of training and testing sets that do not follow the same count label distribution such as the odd-even sets. Intuitively, models that have learned a proper mechanism of counting on odd numbers should perform well on even numbers. Secondly, we introduce the Spatial Counting Network (SCN), which is dedicated to visual analysis and counting based on natural language questions. Our model selects relevant image regions, scores them with fusion and self-attention mechanisms, and provides a final counting score. We apply our protocol on the recent dataset, TallyQA, and show superior performances compared to state-of-the-art models. We also demonstrate the ability of our model to select the correct instances to count in the image. Code and datasets are available: https://github.com/cdancette/spatial-counting-network
Related Topics
Concepts
Spurious relationship
Computer science
Protocol (science)
Artificial intelligence
Task (project management)
Code (set theory)
Statistical model
Counting problem
Image (mathematics)
Machine learning
Pattern recognition (psychology)
Algorithm
Economics
Programming language
Set (abstract data type)
Management
Medicine
Alternative medicine
Pathology
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2006.10079
- https://arxiv.org/pdf/2006.10079
- OA Status
- green
- Cited By
- 3
- References
- 50
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3036605242
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3036605242Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2006.10079Digital Object Identifier
- Title
-
Overcoming Statistical Shortcuts for Open-ended Visual CountingWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-06-17Full publication date if available
- Authors
-
Corentin Dancette, Rémi Cadène, Xinlei Chen, Matthieu CordList of authors in order
- Landing page
-
https://arxiv.org/abs/2006.10079Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2006.10079Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2006.10079Direct OA link when available
- Concepts
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Spurious relationship, Computer science, Protocol (science), Artificial intelligence, Task (project management), Code (set theory), Statistical model, Counting problem, Image (mathematics), Machine learning, Pattern recognition (psychology), Algorithm, Economics, Programming language, Set (abstract data type), Management, Medicine, Alternative medicine, PathologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3Per-year citation counts (last 5 years)
- References (count)
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50Number 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.(MCD) | 70 |
| abstract_inverted_index.Count | 68 |
| abstract_inverted_index.These | 9 |
| abstract_inverted_index.apply | 164 |
| abstract_inverted_index.based | 82, 139 |
| abstract_inverted_index.count | 96, 194 |
| abstract_inverted_index.final | 160 |
| abstract_inverted_index.image | 148 |
| abstract_inverted_index.input | 16 |
| abstract_inverted_index.issue | 30 |
| abstract_inverted_index.label | 97 |
| abstract_inverted_index.learn | 52 |
| abstract_inverted_index.model | 145, 187 |
| abstract_inverted_index.pairs | 84 |
| abstract_inverted_index.parts | 13 |
| abstract_inverted_index.sets. | 103 |
| abstract_inverted_index.study | 43 |
| abstract_inverted_index.which | 38, 72, 131 |
| abstract_inverted_index.(SCN), | 130 |
| abstract_inverted_index.First, | 63 |
| abstract_inverted_index.follow | 93 |
| abstract_inverted_index.fusion | 153 |
| abstract_inverted_index.image. | 197 |
| abstract_inverted_index.label. | 62 |
| abstract_inverted_index.labels | 20 |
| abstract_inverted_index.models | 2, 50, 74, 105 |
| abstract_inverted_index.output | 19, 61 |
| abstract_inverted_index.proper | 54, 110 |
| abstract_inverted_index.recent | 33, 169 |
| abstract_inverted_index.score. | 162 |
| abstract_inverted_index.scores | 150 |
| abstract_inverted_index.select | 189 |
| abstract_inverted_index.should | 117 |
| abstract_inverted_index.suited | 41 |
| abstract_inverted_index.target | 28 |
| abstract_inverted_index.visual | 35, 135 |
| abstract_inverted_index.Machine | 0 |
| abstract_inverted_index.Network | 129 |
| abstract_inverted_index.Spatial | 127 |
| abstract_inverted_index.ability | 184 |
| abstract_inverted_index.between | 12 |
| abstract_inverted_index.correct | 191 |
| abstract_inverted_index.develop | 49 |
| abstract_inverted_index.learned | 108 |
| abstract_inverted_index.models. | 179 |
| abstract_inverted_index.natural | 141 |
| abstract_inverted_index.numbers | 116 |
| abstract_inverted_index.perform | 118 |
| abstract_inverted_index.propose | 65 |
| abstract_inverted_index.selects | 146 |
| abstract_inverted_index.testing | 88 |
| abstract_inverted_index.Counting | 128 |
| abstract_inverted_index.TallyQA, | 171 |
| abstract_inverted_index.analysis | 136 |
| abstract_inverted_index.compared | 176 |
| abstract_inverted_index.counting | 36, 57, 113, 138, 161 |
| abstract_inverted_index.dataset, | 170 |
| abstract_inverted_index.datasets | 200 |
| abstract_inverted_index.language | 142 |
| abstract_inverted_index.learning | 1 |
| abstract_inverted_index.numbers. | 122 |
| abstract_inverted_index.odd-even | 102 |
| abstract_inverted_index.protocol | 166 |
| abstract_inverted_index.provides | 158 |
| abstract_inverted_index.regions, | 149 |
| abstract_inverted_index.relevant | 147 |
| abstract_inverted_index.spurious | 10 |
| abstract_inverted_index.superior | 174 |
| abstract_inverted_index.training | 86 |
| abstract_inverted_index.Modifying | 67 |
| abstract_inverted_index.Secondly, | 123 |
| abstract_inverted_index.dedicated | 133 |
| abstract_inverted_index.instances | 192 |
| abstract_inverted_index.introduce | 125 |
| abstract_inverted_index.mechanism | 55, 111 |
| abstract_inverted_index.over-rely | 5, 76 |
| abstract_inverted_index.penalizes | 73 |
| abstract_inverted_index.protocol, | 71 |
| abstract_inverted_index.settings. | 26 |
| abstract_inverted_index.available: | 202 |
| abstract_inverted_index.open-ended | 34 |
| abstract_inverted_index.questions. | 143 |
| abstract_inverted_index.real-world | 25 |
| abstract_inverted_index.regardless | 58 |
| abstract_inverted_index.shortcuts. | 8, 45, 79 |
| abstract_inverted_index.demonstrate | 182 |
| abstract_inverted_index.mechanisms, | 156 |
| abstract_inverted_index.statistical | 7, 44, 78 |
| abstract_inverted_index.Distribution | 69 |
| abstract_inverted_index.Intuitively, | 104 |
| abstract_inverted_index.correlations | 11 |
| abstract_inverted_index.distribution | 98 |
| abstract_inverted_index.performances | 175 |
| abstract_inverted_index.self-attention | 155 |
| abstract_inverted_index.state-of-the-art | 178 |
| abstract_inverted_index.https://github.com/cdancette/spatial-counting-network | 203 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile |