95,377 research outputs found
Learning Similarity Attention
We consider the problem of learning similarity functions. While there has
been substantial progress in learning suitable distance metrics, these
techniques in general lack decision reasoning, i.e., explaining why the input
set of images is similar or dissimilar. In this work, we solve this key problem
by proposing the first method to generate generic visual similarity
explanations with gradient-based attention. We demonstrate that our technique
is agnostic to the specific similarity model type, e.g., we show applicability
to Siamese, triplet, and quadruplet models. Furthermore, we make our proposed
similarity attention a principled part of the learning process, resulting in a
new paradigm for learning similarity functions. We demonstrate that our
learning mechanism results in more generalizable, as well as explainable,
similarity models. Finally, we demonstrate the generality of our framework by
means of experiments on a variety of tasks, including image retrieval, person
re-identification, and low-shot semantic segmentation.Comment: 10 pages, 7 figures, 4 table
Deep Self-Taught Hashing for Image Retrieval
Hashing algorithm has been widely used to speed up image retrieval due to its compact binary code and fast distance calculation. The combination with deep learning boosts the performance of hashing by learning accurate representations and complicated hashing functions. So far, the most striking success in deep hashing have mostly involved discriminative models, which require labels. To apply deep hashing on datasets without labels, we propose a deep self-taught hashing algorithm (DSTH), which generates a set of pseudo labels by analyzing the data itself, and then learns the hash functions for novel data using discriminative deep models. Furthermore, we generalize DSTH to support both supervised and unsupervised cases by adaptively incorporating label information. We use two different deep learning framework to train the hash functions to deal with out-of-sample problem and reduce the time complexity without loss of accuracy. We have conducted extensive experiments to investigate different settings of DSTH, and compared it with state-of-the-art counterparts in six publicly available datasets. The experimental results show that DSTH outperforms the others in all datasets
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