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Version vom 10. April 2018, 12:24 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: The Web Conference (Cognitive Computing Track)
Verlag: ACM
Referierte Veröffentlichung
BibTeX
Kurzfassung
We address the task of labeling image-sentence pair at large-scale
with varied concepts representing connotations. That is for any
given query image-sentence, we aim to annotate them with the
connotations that capture intrinsic intension. To achieve it, we pro-
pose a Connotation multimodal embedding model (CMEM) with a
novel loss function. Its unique characteristics over previous models
include (i) can leverage multimodal data as opposed to only visual
information, (ii) robust to outlier labels in a multi-label scenario
and (iii) works well 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 images
with connotation labels, our byproduct of the model inherently
supports cross-modal retrieval.
Download: Media:Ctp147-mogadalaA.pdf
Information Retrieval, Maschinelles Lernen, Künstliche Intelligenz, WWW Systeme