9,872 research outputs found
Auto-Encoding Scene Graphs for Image Captioning
We propose Scene Graph Auto-Encoder (SGAE) that incorporates the language
inductive bias into the encoder-decoder image captioning framework for more
human-like captions. Intuitively, we humans use the inductive bias to compose
collocations and contextual inference in discourse. For example, when we see
the relation `person on bike', it is natural to replace `on' with `ride' and
infer `person riding bike on a road' even the `road' is not evident. Therefore,
exploiting such bias as a language prior is expected to help the conventional
encoder-decoder models less likely overfit to the dataset bias and focus on
reasoning. Specifically, we use the scene graph --- a directed graph
() where an object node is connected by adjective nodes and
relationship nodes --- to represent the complex structural layout of both image
() and sentence (). In the textual domain, we use
SGAE to learn a dictionary () that helps to reconstruct sentences
in the pipeline, where encodes the desired language prior;
in the vision-language domain, we use the shared to guide the
encoder-decoder in the pipeline. Thanks to the scene graph
representation and shared dictionary, the inductive bias is transferred across
domains in principle. We validate the effectiveness of SGAE on the challenging
MS-COCO image captioning benchmark, e.g., our SGAE-based single-model achieves
a new state-of-the-art CIDEr-D on the Karpathy split, and a competitive
CIDEr-D (c40) on the official server even compared to other ensemble
models
Learning Social Image Embedding with Deep Multimodal Attention Networks
Learning social media data embedding by deep models has attracted extensive
research interest as well as boomed a lot of applications, such as link
prediction, classification, and cross-modal search. However, for social images
which contain both link information and multimodal contents (e.g., text
description, and visual content), simply employing the embedding learnt from
network structure or data content results in sub-optimal social image
representation. In this paper, we propose a novel social image embedding
approach called Deep Multimodal Attention Networks (DMAN), which employs a deep
model to jointly embed multimodal contents and link information. Specifically,
to effectively capture the correlations between multimodal contents, we propose
a multimodal attention network to encode the fine-granularity relation between
image regions and textual words. To leverage the network structure for
embedding learning, a novel Siamese-Triplet neural network is proposed to model
the links among images. With the joint deep model, the learnt embedding can
capture both the multimodal contents and the nonlinear network information.
Extensive experiments are conducted to investigate the effectiveness of our
approach in the applications of multi-label classification and cross-modal
search. Compared to state-of-the-art image embeddings, our proposed DMAN
achieves significant improvement in the tasks of multi-label classification and
cross-modal search
Simple to Complex Cross-modal Learning to Rank
The heterogeneity-gap between different modalities brings a significant
challenge to multimedia information retrieval. Some studies formalize the
cross-modal retrieval tasks as a ranking problem and learn a shared multi-modal
embedding space to measure the cross-modality similarity. However, previous
methods often establish the shared embedding space based on linear mapping
functions which might not be sophisticated enough to reveal more complicated
inter-modal correspondences. Additionally, current studies assume that the
rankings are of equal importance, and thus all rankings are used
simultaneously, or a small number of rankings are selected randomly to train
the embedding space at each iteration. Such strategies, however, always suffer
from outliers as well as reduced generalization capability due to their lack of
insightful understanding of procedure of human cognition. In this paper, we
involve the self-paced learning theory with diversity into the cross-modal
learning to rank and learn an optimal multi-modal embedding space based on
non-linear mapping functions. This strategy enhances the model's robustness to
outliers and achieves better generalization via training the model gradually
from easy rankings by diverse queries to more complex ones. An efficient
alternative algorithm is exploited to solve the proposed challenging problem
with fast convergence in practice. Extensive experimental results on several
benchmark datasets indicate that the proposed method achieves significant
improvements over the state-of-the-arts in this literature.Comment: 14 pages; Accepted by Computer Vision and Image Understandin
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