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|Title=Discovering Connotations as Labels for Weakly Supervised Image-Sentence Data | |Title=Discovering Connotations as Labels for Weakly Supervised Image-Sentence Data | ||
|Year=2018 | |Year=2018 | ||
|Month=April | |Month=April | ||
− | |Booktitle=The Web Conference | + | |Booktitle=WWW'18: Proceedings of The Web Conference 2018, Lyon, France, April 2018 |
+ | |Pages=379-386 | ||
|Publisher=ACM | |Publisher=ACM | ||
}} | }} | ||
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|Abstract=Growth of multimodal content on the web and social media has | |Abstract=Growth of multimodal content on the web and social media has | ||
generated abundant weakly aligned image-sentence pairs. However, it is hard to interpret them directly due to intrinsic “intension”. In this paper, we aim to annotate such image-sentence pairs with connotations as labels to capture the intrinsic “intension”. We achieve it with a connotation multimodal embedding model (CMEM) using a novel loss function. It’s unique characteristics over previous models include: (i) the exploitation of multimodal data as opposed to only visual information, (ii) robustness to outlier labels in a multi-label scenario and (iii) works effectively with large-scale weakly supervised data. With extensive quantitative evaluation, we exhibit the effectiveness of CMEM for detection of multiple labels over other state-of-the-art approaches. Also, we show that in addition to annotation of image-sentence pairs with connotation labels, byproduct of our model inherently supports cross-modal retrieval i.e. image query - sentence retrieval. | generated abundant weakly aligned image-sentence pairs. However, it is hard to interpret them directly due to intrinsic “intension”. In this paper, we aim to annotate such image-sentence pairs with connotations as labels to capture the intrinsic “intension”. We achieve it with a connotation multimodal embedding model (CMEM) using a novel loss function. It’s unique characteristics over previous models include: (i) the exploitation of multimodal data as opposed to only visual information, (ii) robustness to outlier labels in a multi-label scenario and (iii) works effectively with large-scale weakly supervised data. With extensive quantitative evaluation, we exhibit the effectiveness of CMEM for detection of multiple labels over other state-of-the-art approaches. Also, we show that in addition to annotation of image-sentence pairs with connotation labels, byproduct of our model inherently supports cross-modal retrieval i.e. image query - sentence retrieval. | ||
+ | |ISBN=978-1-4503-5640-4 | ||
|Download=Ctp147-mogadalaA.pdf, | |Download=Ctp147-mogadalaA.pdf, | ||
+ | |Link=https://dl.acm.org/citation.cfm?id=3184558.3186352 | ||
+ | |DOI Name=10.1145/3184558.3186352 | ||
|Forschungsgruppe=Web Science | |Forschungsgruppe=Web Science | ||
}} | }} |
Version vom 7. Juni 2018, 15:31 Uhr
Discovering Connotations as Labels for Weakly Supervised Image-Sentence Data
Discovering Connotations as Labels for Weakly Supervised Image-Sentence Data
Published: 2018
April
Buchtitel: WWW'18: Proceedings of The Web Conference 2018, Lyon, France, April 2018
Seiten: 379-386
Verlag: ACM
Referierte Veröffentlichung
BibTeX
Kurzfassung
Growth of multimodal content on the web and social media has
generated abundant weakly aligned image-sentence pairs. However, it is hard to interpret them directly due to intrinsic “intension”. In this paper, we aim to annotate such image-sentence pairs with connotations as labels to capture the intrinsic “intension”. We achieve it with a connotation multimodal embedding model (CMEM) using a novel loss function. It’s unique characteristics over previous models include: (i) the exploitation of multimodal data as opposed to only visual information, (ii) robustness to outlier labels in a multi-label scenario and (iii) works effectively with large-scale weakly supervised data. With extensive quantitative evaluation, we exhibit the effectiveness of CMEM for detection of multiple labels over other state-of-the-art approaches. Also, we show that in addition to annotation of image-sentence pairs with connotation labels, byproduct of our model inherently supports cross-modal retrieval i.e. image query - sentence retrieval.
ISBN: 978-1-4503-5640-4
Download: Media:Ctp147-mogadalaA.pdf
Weitere Informationen unter: Link
DOI Link: 10.1145/3184558.3186352
Information Retrieval, Maschinelles Lernen, Künstliche Intelligenz, WWW Systeme