146 research outputs found
Learning a Complete Image Indexing Pipeline
To work at scale, a complete image indexing system comprises two components:
An inverted file index to restrict the actual search to only a subset that
should contain most of the items relevant to the query; An approximate distance
computation mechanism to rapidly scan these lists. While supervised deep
learning has recently enabled improvements to the latter, the former continues
to be based on unsupervised clustering in the literature. In this work, we
propose a first system that learns both components within a unifying neural
framework of structured binary encoding
Self-Supervised Video Hashing with Hierarchical Binary Auto-encoder
Existing video hash functions are built on three isolated stages: frame
pooling, relaxed learning, and binarization, which have not adequately explored
the temporal order of video frames in a joint binary optimization model,
resulting in severe information loss. In this paper, we propose a novel
unsupervised video hashing framework dubbed Self-Supervised Video Hashing
(SSVH), that is able to capture the temporal nature of videos in an end-to-end
learning-to-hash fashion. We specifically address two central problems: 1) how
to design an encoder-decoder architecture to generate binary codes for videos;
and 2) how to equip the binary codes with the ability of accurate video
retrieval. We design a hierarchical binary autoencoder to model the temporal
dependencies in videos with multiple granularities, and embed the videos into
binary codes with less computations than the stacked architecture. Then, we
encourage the binary codes to simultaneously reconstruct the visual content and
neighborhood structure of the videos. Experiments on two real-world datasets
(FCVID and YFCC) show that our SSVH method can significantly outperform the
state-of-the-art methods and achieve the currently best performance on the task
of unsupervised video retrieval
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