2,888 research outputs found
Ranking-based Deep Cross-modal Hashing
Cross-modal hashing has been receiving increasing interests for its low
storage cost and fast query speed in multi-modal data retrievals. However, most
existing hashing methods are based on hand-crafted or raw level features of
objects, which may not be optimally compatible with the coding process.
Besides, these hashing methods are mainly designed to handle simple pairwise
similarity. The complex multilevel ranking semantic structure of instances
associated with multiple labels has not been well explored yet. In this paper,
we propose a ranking-based deep cross-modal hashing approach (RDCMH). RDCMH
firstly uses the feature and label information of data to derive a
semi-supervised semantic ranking list. Next, to expand the semantic
representation power of hand-crafted features, RDCMH integrates the semantic
ranking information into deep cross-modal hashing and jointly optimizes the
compatible parameters of deep feature representations and of hashing functions.
Experiments on real multi-modal datasets show that RDCMH outperforms other
competitive baselines and achieves the state-of-the-art performance in
cross-modal retrieval applications
Visual Search at eBay
In this paper, we propose a novel end-to-end approach for scalable visual
search infrastructure. We discuss the challenges we faced for a massive
volatile inventory like at eBay and present our solution to overcome those. We
harness the availability of large image collection of eBay listings and
state-of-the-art deep learning techniques to perform visual search at scale.
Supervised approach for optimized search limited to top predicted categories
and also for compact binary signature are key to scale up without compromising
accuracy and precision. Both use a common deep neural network requiring only a
single forward inference. The system architecture is presented with in-depth
discussions of its basic components and optimizations for a trade-off between
search relevance and latency. This solution is currently deployed in a
distributed cloud infrastructure and fuels visual search in eBay ShopBot and
Close5. We show benchmark on ImageNet dataset on which our approach is faster
and more accurate than several unsupervised baselines. We share our learnings
with the hope that visual search becomes a first class citizen for all large
scale search engines rather than an afterthought.Comment: To appear in 23rd SIGKDD Conference on Knowledge Discovery and Data
Mining (KDD), 2017. A demonstration video can be found at
https://youtu.be/iYtjs32vh4
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