8,423 research outputs found
Learning to Hash-tag Videos with Tag2Vec
User-given tags or labels are valuable resources for semantic understanding
of visual media such as images and videos. Recently, a new type of labeling
mechanism known as hash-tags have become increasingly popular on social media
sites. In this paper, we study the problem of generating relevant and useful
hash-tags for short video clips. Traditional data-driven approaches for tag
enrichment and recommendation use direct visual similarity for label transfer
and propagation. We attempt to learn a direct low-cost mapping from video to
hash-tags using a two step training process. We first employ a natural language
processing (NLP) technique, skip-gram models with neural network training to
learn a low-dimensional vector representation of hash-tags (Tag2Vec) using a
corpus of 10 million hash-tags. We then train an embedding function to map
video features to the low-dimensional Tag2vec space. We learn this embedding
for 29 categories of short video clips with hash-tags. A query video without
any tag-information can then be directly mapped to the vector space of tags
using the learned embedding and relevant tags can be found by performing a
simple nearest-neighbor retrieval in the Tag2Vec space. We validate the
relevance of the tags suggested by our system qualitatively and quantitatively
with a user study
Exploiting multimedia in creating and analysing multimedia Web archives
The data contained on the web and the social web are inherently multimedia and consist of a mixture of textual, visual and audio modalities. Community memories embodied on the web and social web contain a rich mixture of data from these modalities. In many ways, the web is the greatest resource ever created by human-kind. However, due to the dynamic and distributed nature of the web, its content changes, appears and disappears on a daily basis. Web archiving provides a way of capturing snapshots of (parts of) the web for preservation and future analysis. This paper provides an overview of techniques we have developed within the context of the EU funded ARCOMEM (ARchiving COmmunity MEMories) project to allow multimedia web content to be leveraged during the archival process and for post-archival analysis. Through a set of use cases, we explore several practical applications of multimedia analytics within the realm of web archiving, web archive analysis and multimedia data on the web in general
Cross-Domain Image Retrieval with Attention Modeling
With the proliferation of e-commerce websites and the ubiquitousness of smart
phones, cross-domain image retrieval using images taken by smart phones as
queries to search products on e-commerce websites is emerging as a popular
application. One challenge of this task is to locate the attention of both the
query and database images. In particular, database images, e.g. of fashion
products, on e-commerce websites are typically displayed with other
accessories, and the images taken by users contain noisy background and large
variations in orientation and lighting. Consequently, their attention is
difficult to locate. In this paper, we exploit the rich tag information
available on the e-commerce websites to locate the attention of database
images. For query images, we use each candidate image in the database as the
context to locate the query attention. Novel deep convolutional neural network
architectures, namely TagYNet and CtxYNet, are proposed to learn the attention
weights and then extract effective representations of the images. Experimental
results on public datasets confirm that our approaches have significant
improvement over the existing methods in terms of the retrieval accuracy and
efficiency.Comment: 8 pages with an extra reference pag
Composite Correlation Quantization for Efficient Multimodal Retrieval
Efficient similarity retrieval from large-scale multimodal database is
pervasive in modern search engines and social networks. To support queries
across content modalities, the system should enable cross-modal correlation and
computation-efficient indexing. While hashing methods have shown great
potential in achieving this goal, current attempts generally fail to learn
isomorphic hash codes in a seamless scheme, that is, they embed multiple
modalities in a continuous isomorphic space and separately threshold embeddings
into binary codes, which incurs substantial loss of retrieval accuracy. In this
paper, we approach seamless multimodal hashing by proposing a novel Composite
Correlation Quantization (CCQ) model. Specifically, CCQ jointly finds
correlation-maximal mappings that transform different modalities into
isomorphic latent space, and learns composite quantizers that convert the
isomorphic latent features into compact binary codes. An optimization framework
is devised to preserve both intra-modal similarity and inter-modal correlation
through minimizing both reconstruction and quantization errors, which can be
trained from both paired and partially paired data in linear time. A
comprehensive set of experiments clearly show the superior effectiveness and
efficiency of CCQ against the state of the art hashing methods for both
unimodal and cross-modal retrieval
Integrating Information Theory and Adversarial Learning for Cross-modal Retrieval
Accurately matching visual and textual data in cross-modal retrieval has been
widely studied in the multimedia community. To address these challenges posited
by the heterogeneity gap and the semantic gap, we propose integrating Shannon
information theory and adversarial learning. In terms of the heterogeneity gap,
we integrate modality classification and information entropy maximization
adversarially. For this purpose, a modality classifier (as a discriminator) is
built to distinguish the text and image modalities according to their different
statistical properties. This discriminator uses its output probabilities to
compute Shannon information entropy, which measures the uncertainty of the
modality classification it performs. Moreover, feature encoders (as a
generator) project uni-modal features into a commonly shared space and attempt
to fool the discriminator by maximizing its output information entropy. Thus,
maximizing information entropy gradually reduces the distribution discrepancy
of cross-modal features, thereby achieving a domain confusion state where the
discriminator cannot classify two modalities confidently. To reduce the
semantic gap, Kullback-Leibler (KL) divergence and bi-directional triplet loss
are used to associate the intra- and inter-modality similarity between features
in the shared space. Furthermore, a regularization term based on KL-divergence
with temperature scaling is used to calibrate the biased label classifier
caused by the data imbalance issue. Extensive experiments with four deep models
on four benchmarks are conducted to demonstrate the effectiveness of the
proposed approach.Comment: Accepted by Pattern Recognitio
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