Галерея 3157481
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Галерея 3157481
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6-Fluoro-1H-benzoimidazole-2-carboxylic acid
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8 Classification Expand this section
Computed by Lexichem TK 2.7.0 (PubChem release 2021.05.07)
Computed by InChI 1.0.6 (PubChem release 2021.05.07)
Computed by InChI 1.0.6 (PubChem release 2021.05.07)
Computed by OEChem 2.3.0 (PubChem release 2021.05.07)
Computed by PubChem 2.1 (PubChem release 2021.05.07)
6-Fluoro-1H-benzimidazole-2-carboxylic acid
5-FLUORO-1H-BENZIMIDAZOLE-2-CARBOXYLIC-ACID
6-Fluoro-1H-benzimidazole-2-carboxylic acid
PFAS and Fluorinated Compounds in PubChem
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6-Fluoro-1H-benzoimidazole-2-carboxylic acid
5-fluoro-1H-benzimidazole-2-carboxylic acid
6-fluoro-1H-benzimidazole-2-carboxylic acid
1H-Benzimidazole-2-carboxylicacid, 6-fluoro-
6-fluoro-1 H -benzimidazole-2-carboxylic acid
InChI=1S/C8H5FN2O2/c9-4-1-2-5-6(3-4)11-7(10-5)8(12)13/h1-3H,(H,10,11)(H,12,13)
Patents are available for this chemical structure:
Computed by PubChem 2.1 (PubChem release 2021.05.07)
Computed by XLogP3 3.0 (PubChem release 2021.05.07)
Computed by Cactvs 3.4.8.18 (PubChem release 2021.05.07)
Computed by Cactvs 3.4.8.18 (PubChem release 2021.05.07)
Computed by Cactvs 3.4.8.18 (PubChem release 2021.05.07)
Computed by PubChem 2.1 (PubChem release 2021.05.07)
Computed by PubChem 2.1 (PubChem release 2021.05.07)
Computed by Cactvs 3.4.8.18 (PubChem release 2021.05.07)
Computed by Cactvs 3.4.8.18 (PubChem release 2021.05.07)
Computed by PubChem (release 2012.11.26)
Copyright © 2016-2021 W. Robien, Inst. of Org. Chem., Univ. of Vienna. All Rights Reserved.
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Abstract: Weakly supervised semantic segmentation with only image-level labels aims to reduce annotation costs for the segmentation task. Existing approaches generally leverage cla... View more
Weakly supervised semantic segmentation with only image-level labels aims to reduce annotation costs for the segmentation task. Existing approaches generally leverage class activation maps (CAMs) to locate the object regions for pseudo label generation. However, CAMs can only discover the most discriminative parts of objects, thus leading to inferior pixel-level pseudo labels. To address this issue, we propose a saliency guided I nter- and I ntra- C lass R elation C onstrained (I 2 CRC) framework to assist the expansion of the activated object regions in CAMs. Specifically, we propose a saliency guided class-agnostic distance module to pull the intra-category features closer by aligning features to their class prototypes. Further, we propose a class-specific distance module to push the inter-class features apart and encourage the object region to have a higher activation than the background. Besides strengthening the capability of the classification network to activate more integral object regions in CAMs, we also introduce an object guided label refinement module to take a full use of both the segmentation prediction and the initial labels for obtaining superior pseudo-labels. Extensive experiments on PASCAL VOC 2012 and COCO datasets demonstrate well the effectiveness of I 2 CRC over other state-of-the-art counterparts.
Published in: IEEE Transactions on Multimedia ( Early Access )
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