4,719 research outputs found
Deep Binary Reconstruction for Cross-modal Hashing
With the increasing demand of massive multimodal data storage and
organization, cross-modal retrieval based on hashing technique has drawn much
attention nowadays. It takes the binary codes of one modality as the query to
retrieve the relevant hashing codes of another modality. However, the existing
binary constraint makes it difficult to find the optimal cross-modal hashing
function. Most approaches choose to relax the constraint and perform
thresholding strategy on the real-value representation instead of directly
solving the original objective. In this paper, we first provide a concrete
analysis about the effectiveness of multimodal networks in preserving the
inter- and intra-modal consistency. Based on the analysis, we provide a
so-called Deep Binary Reconstruction (DBRC) network that can directly learn the
binary hashing codes in an unsupervised fashion. The superiority comes from a
proposed simple but efficient activation function, named as Adaptive Tanh
(ATanh). The ATanh function can adaptively learn the binary codes and be
trained via back-propagation. Extensive experiments on three benchmark datasets
demonstrate that DBRC outperforms several state-of-the-art methods in both
image2text and text2image retrieval task.Comment: 8 pages, 5 figures, accepted by ACM Multimedia 201
Improving Distributed Representations of Tweets - Present and Future
Unsupervised representation learning for tweets is an important research
field which helps in solving several business applications such as sentiment
analysis, hashtag prediction, paraphrase detection and microblog ranking. A
good tweet representation learning model must handle the idiosyncratic nature
of tweets which poses several challenges such as short length, informal words,
unusual grammar and misspellings. However, there is a lack of prior work which
surveys the representation learning models with a focus on tweets. In this
work, we organize the models based on its objective function which aids the
understanding of the literature. We also provide interesting future directions,
which we believe are fruitful in advancing this field by building high-quality
tweet representation learning models.Comment: To be presented in Student Research Workshop (SRW) at ACL 201
Improving Distributed Representations of Tweets - Present and Future
Unsupervised representation learning for tweets is an important research
field which helps in solving several business applications such as sentiment
analysis, hashtag prediction, paraphrase detection and microblog ranking. A
good tweet representation learning model must handle the idiosyncratic nature
of tweets which poses several challenges such as short length, informal words,
unusual grammar and misspellings. However, there is a lack of prior work which
surveys the representation learning models with a focus on tweets. In this
work, we organize the models based on its objective function which aids the
understanding of the literature. We also provide interesting future directions,
which we believe are fruitful in advancing this field by building high-quality
tweet representation learning models.Comment: To be presented in Student Research Workshop (SRW) at ACL 201
Multimedia information technology and the annotation of video
The state of the art in multimedia information technology has not progressed to the point where a single solution is available to meet all reasonable needs of documentalists and users of video archives. In general, we do not have an optimistic view of the usability of new technology in this domain, but digitization and digital power can be expected to cause a small revolution in the area of video archiving. The volume of data leads to two views of the future: on the pessimistic side, overload of data will cause lack of annotation capacity, and on the optimistic side, there will be enough data from which to learn selected concepts that can be deployed to support automatic annotation. At the threshold of this interesting era, we make an attempt to describe the state of the art in technology. We sample the progress in text, sound, and image processing, as well as in machine learning
- …