Category-Aware Saliency Enhance Learning Based on CLIP for Weakly Supervised Salient Object Detection Article Swipe
Yunde Zhang
,
Zhili Zhang
,
Tianshan Liu
,
Jun Kong
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1007/s11063-024-11530-2
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1007/s11063-024-11530-2
Weakly supervised salient object detection (SOD) using image-level category labels has been proposed to reduce the annotation cost of pixel-level labels. However, existing methods mostly train a classification network to generate a class activation map, which suffers from coarse localization and difficult pseudo-label updating. To address these issues, we propose a novel Category-aware Saliency Enhance Learning (CSEL) method based on contrastive vision-language pre-training (CLIP), which can perform image-text classification and pseudo-label updating simultaneously. Our proposed method transforms image-text classification into pixel-text matching and generates a category-aware saliency map, which is evaluated by the classification accuracy. Moreover, CSEL assesses the quality of the category-aware saliency map and the pseudo saliency map, and uses the quality confidence scores as weights to update the pseudo labels. The two maps mutually enhance each other to guide the pseudo saliency map in the correct direction. Our SOD network can be trained jointly under the supervision of the updated pseudo saliency maps. We test our model on various well-known RGB-D and RGB SOD datasets. Our model achieves an S-measure of 87.6 $$\%$$ on the RGB-D NLPR dataset and 84.3 $$\%$$ on the RGB ECSSD dataset. Additionally, we obtain satisfactory performance on the weakly supervised E-measure, F-measure, and mean absolute error metrics for other datasets. These results demonstrate the effectiveness of our model.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s11063-024-11530-2
- https://link.springer.com/content/pdf/10.1007/s11063-024-11530-2.pdf
- OA Status
- hybrid
- Cited By
- 3
- References
- 55
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4391885979
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4391885979Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1007/s11063-024-11530-2Digital Object Identifier
- Title
-
Category-Aware Saliency Enhance Learning Based on CLIP for Weakly Supervised Salient Object DetectionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-02-16Full publication date if available
- Authors
-
Yunde Zhang, Zhili Zhang, Tianshan Liu, Jun KongList of authors in order
- Landing page
-
https://doi.org/10.1007/s11063-024-11530-2Publisher landing page
- PDF URL
-
https://link.springer.com/content/pdf/10.1007/s11063-024-11530-2.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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hybridOpen access status per OpenAlex
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https://link.springer.com/content/pdf/10.1007/s11063-024-11530-2.pdfDirect OA link when available
- Concepts
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Computational intelligence, Artificial intelligence, Salient, Object (grammar), Computer science, Object detection, Pattern recognition (psychology), Computer vision, Machine learningTop 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: 2, 2024: 1Per-year citation counts (last 5 years)
- References (count)
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55Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| publication_date | 2024-02-16 |
| publication_year | 2024 |
| referenced_works | https://openalex.org/W4281710415, https://openalex.org/W2610074871, https://openalex.org/W4226305381, https://openalex.org/W3120432545, https://openalex.org/W4214506192, https://openalex.org/W3162412530, https://openalex.org/W2740667773, https://openalex.org/W2963020481, https://openalex.org/W2963136160, https://openalex.org/W2954204101, https://openalex.org/W4221161499, https://openalex.org/W3156154235, https://openalex.org/W4221167637, https://openalex.org/W3120636940, https://openalex.org/W4212823076, https://openalex.org/W2108598243, https://openalex.org/W1861492603, https://openalex.org/W2295107390, https://openalex.org/W2962858109, https://openalex.org/W3177087374, https://openalex.org/W4213441760, https://openalex.org/W3204446219, https://openalex.org/W4383619701, https://openalex.org/W3212645988, https://openalex.org/W3214999242, https://openalex.org/W4313529404, https://openalex.org/W4312818263, https://openalex.org/W4310557340, https://openalex.org/W4220700494, https://openalex.org/W6600140277, https://openalex.org/W4312458986, https://openalex.org/W4312563428, https://openalex.org/W4312956471, https://openalex.org/W4226058394, https://openalex.org/W4312980231, https://openalex.org/W2998449272, https://openalex.org/W2962741298, https://openalex.org/W3097053213, https://openalex.org/W1976409045, https://openalex.org/W20683899, https://openalex.org/W2957414648, https://openalex.org/W4296031724, https://openalex.org/W4362496243, https://openalex.org/W3002301267, https://openalex.org/W3108608656, https://openalex.org/W4285163934, https://openalex.org/W3151922143, https://openalex.org/W2002781701, https://openalex.org/W2039313011, https://openalex.org/W2086791339, https://openalex.org/W2963868681, https://openalex.org/W2963529609, https://openalex.org/W4213078714, https://openalex.org/W1982075130, https://openalex.org/W4226125224 |
| referenced_works_count | 55 |
| abstract_inverted_index.a | 27, 32, 51, 85 |
| abstract_inverted_index.To | 45 |
| abstract_inverted_index.We | 157 |
| abstract_inverted_index.an | 172 |
| abstract_inverted_index.as | 117 |
| abstract_inverted_index.be | 145 |
| abstract_inverted_index.by | 92 |
| abstract_inverted_index.in | 137 |
| abstract_inverted_index.is | 90 |
| abstract_inverted_index.of | 19, 101, 151, 174, 222 |
| abstract_inverted_index.on | 60, 161, 181, 193, 203 |
| abstract_inverted_index.to | 14, 30, 119, 131 |
| abstract_inverted_index.we | 49, 199 |
| abstract_inverted_index.Our | 74, 141, 169 |
| abstract_inverted_index.RGB | 166, 195 |
| abstract_inverted_index.SOD | 142, 167 |
| abstract_inverted_index.The | 124 |
| abstract_inverted_index.and | 41, 70, 83, 106, 111, 165, 186, 209 |
| abstract_inverted_index.can | 66, 144 |
| abstract_inverted_index.for | 214 |
| abstract_inverted_index.has | 11 |
| abstract_inverted_index.map | 105, 136 |
| abstract_inverted_index.our | 159, 223 |
| abstract_inverted_index.the | 16, 93, 99, 102, 107, 113, 121, 133, 138, 149, 152, 182, 194, 204, 220 |
| abstract_inverted_index.two | 125 |
| abstract_inverted_index.84.3 | 187 |
| abstract_inverted_index.87.6 | 175 |
| abstract_inverted_index.CSEL | 97 |
| abstract_inverted_index.NLPR | 184 |
| abstract_inverted_index.been | 12 |
| abstract_inverted_index.cost | 18 |
| abstract_inverted_index.each | 129 |
| abstract_inverted_index.from | 38 |
| abstract_inverted_index.into | 80 |
| abstract_inverted_index.map, | 35, 88, 110 |
| abstract_inverted_index.maps | 126 |
| abstract_inverted_index.mean | 210 |
| abstract_inverted_index.test | 158 |
| abstract_inverted_index.uses | 112 |
| abstract_inverted_index.(SOD) | 6 |
| abstract_inverted_index.ECSSD | 196 |
| abstract_inverted_index.RGB-D | 164, 183 |
| abstract_inverted_index.These | 217 |
| abstract_inverted_index.based | 59 |
| abstract_inverted_index.class | 33 |
| abstract_inverted_index.error | 212 |
| abstract_inverted_index.guide | 132 |
| abstract_inverted_index.maps. | 156 |
| abstract_inverted_index.model | 160, 170 |
| abstract_inverted_index.novel | 52 |
| abstract_inverted_index.other | 130, 215 |
| abstract_inverted_index.these | 47 |
| abstract_inverted_index.train | 26 |
| abstract_inverted_index.under | 148 |
| abstract_inverted_index.using | 7 |
| abstract_inverted_index.which | 36, 65, 89 |
| abstract_inverted_index.$$\%$$ | 176, 188 |
| abstract_inverted_index.(CSEL) | 57 |
| abstract_inverted_index.Weakly | 1 |
| abstract_inverted_index.coarse | 39 |
| abstract_inverted_index.labels | 10 |
| abstract_inverted_index.method | 58, 76 |
| abstract_inverted_index.model. | 224 |
| abstract_inverted_index.mostly | 25 |
| abstract_inverted_index.object | 4 |
| abstract_inverted_index.obtain | 200 |
| abstract_inverted_index.pseudo | 108, 122, 134, 154 |
| abstract_inverted_index.reduce | 15 |
| abstract_inverted_index.scores | 116 |
| abstract_inverted_index.update | 120 |
| abstract_inverted_index.weakly | 205 |
| abstract_inverted_index.(CLIP), | 64 |
| abstract_inverted_index.Enhance | 55 |
| abstract_inverted_index.address | 46 |
| abstract_inverted_index.correct | 139 |
| abstract_inverted_index.dataset | 185 |
| abstract_inverted_index.enhance | 128 |
| abstract_inverted_index.issues, | 48 |
| abstract_inverted_index.jointly | 147 |
| abstract_inverted_index.labels. | 21, 123 |
| abstract_inverted_index.methods | 24 |
| abstract_inverted_index.metrics | 213 |
| abstract_inverted_index.network | 29, 143 |
| abstract_inverted_index.perform | 67 |
| abstract_inverted_index.propose | 50 |
| abstract_inverted_index.quality | 100, 114 |
| abstract_inverted_index.results | 218 |
| abstract_inverted_index.salient | 3 |
| abstract_inverted_index.suffers | 37 |
| abstract_inverted_index.trained | 146 |
| abstract_inverted_index.updated | 153 |
| abstract_inverted_index.various | 162 |
| abstract_inverted_index.weights | 118 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.However, | 22 |
| abstract_inverted_index.Learning | 56 |
| abstract_inverted_index.Saliency | 54 |
| abstract_inverted_index.absolute | 211 |
| abstract_inverted_index.achieves | 171 |
| abstract_inverted_index.assesses | 98 |
| abstract_inverted_index.category | 9 |
| abstract_inverted_index.dataset. | 197 |
| abstract_inverted_index.existing | 23 |
| abstract_inverted_index.generate | 31 |
| abstract_inverted_index.matching | 82 |
| abstract_inverted_index.mutually | 127 |
| abstract_inverted_index.proposed | 13, 75 |
| abstract_inverted_index.saliency | 87, 104, 109, 135, 155 |
| abstract_inverted_index.updating | 72 |
| abstract_inverted_index.<mml:math | 177, 189 |
| abstract_inverted_index.Moreover, | 96 |
| abstract_inverted_index.S-measure | 173 |
| abstract_inverted_index.accuracy. | 95 |
| abstract_inverted_index.datasets. | 168, 216 |
| abstract_inverted_index.detection | 5 |
| abstract_inverted_index.difficult | 42 |
| abstract_inverted_index.evaluated | 91 |
| abstract_inverted_index.generates | 84 |
| abstract_inverted_index.updating. | 44 |
| abstract_inverted_index.E-measure, | 207 |
| abstract_inverted_index.F-measure, | 208 |
| abstract_inverted_index.activation | 34 |
| abstract_inverted_index.annotation | 17 |
| abstract_inverted_index.confidence | 115 |
| abstract_inverted_index.direction. | 140 |
| abstract_inverted_index.image-text | 68, 78 |
| abstract_inverted_index.pixel-text | 81 |
| abstract_inverted_index.supervised | 2, 206 |
| abstract_inverted_index.transforms | 77 |
| abstract_inverted_index.well-known | 163 |
| abstract_inverted_index.</mml:math> | 180, 192 |
| abstract_inverted_index.contrastive | 61 |
| abstract_inverted_index.demonstrate | 219 |
| abstract_inverted_index.image-level | 8 |
| abstract_inverted_index.performance | 202 |
| abstract_inverted_index.pixel-level | 20 |
| abstract_inverted_index.supervision | 150 |
| abstract_inverted_index.localization | 40 |
| abstract_inverted_index.pre-training | 63 |
| abstract_inverted_index.pseudo-label | 43, 71 |
| abstract_inverted_index.satisfactory | 201 |
| abstract_inverted_index.Additionally, | 198 |
| abstract_inverted_index.effectiveness | 221 |
| abstract_inverted_index.Category-aware | 53 |
| abstract_inverted_index.category-aware | 86, 103 |
| abstract_inverted_index.classification | 28, 69, 79, 94 |
| abstract_inverted_index.simultaneously. | 73 |
| abstract_inverted_index.vision-language | 62 |
| abstract_inverted_index.<mml:mo>%</mml:mo> | 179, 191 |
| abstract_inverted_index.xmlns:mml="http://www.w3.org/1998/Math/MathML"> | 178, 190 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.5799999833106995 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile.value | 0.75291856 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |