2,676 research outputs found
Fast k-means based on KNN Graph
In the era of big data, k-means clustering has been widely adopted as a basic
processing tool in various contexts. However, its computational cost could be
prohibitively high as the data size and the cluster number are large. It is
well known that the processing bottleneck of k-means lies in the operation of
seeking closest centroid in each iteration. In this paper, a novel solution
towards the scalability issue of k-means is presented. In the proposal, k-means
is supported by an approximate k-nearest neighbors graph. In the k-means
iteration, each data sample is only compared to clusters that its nearest
neighbors reside. Since the number of nearest neighbors we consider is much
less than k, the processing cost in this step becomes minor and irrelevant to
k. The processing bottleneck is therefore overcome. The most interesting thing
is that k-nearest neighbor graph is constructed by iteratively calling the fast
-means itself. Comparing with existing fast k-means variants, the proposed
algorithm achieves hundreds to thousands times speed-up while maintaining high
clustering quality. As it is tested on 10 million 512-dimensional data, it
takes only 5.2 hours to produce 1 million clusters. In contrast, to fulfill the
same scale of clustering, it would take 3 years for traditional k-means
Language Models for Image Captioning: The Quirks and What Works
Two recent approaches have achieved state-of-the-art results in image
captioning. The first uses a pipelined process where a set of candidate words
is generated by a convolutional neural network (CNN) trained on images, and
then a maximum entropy (ME) language model is used to arrange these words into
a coherent sentence. The second uses the penultimate activation layer of the
CNN as input to a recurrent neural network (RNN) that then generates the
caption sequence. In this paper, we compare the merits of these different
language modeling approaches for the first time by using the same
state-of-the-art CNN as input. We examine issues in the different approaches,
including linguistic irregularities, caption repetition, and data set overlap.
By combining key aspects of the ME and RNN methods, we achieve a new record
performance over previously published results on the benchmark COCO dataset.
However, the gains we see in BLEU do not translate to human judgments.Comment: See http://research.microsoft.com/en-us/projects/image_captioning for
project informatio
Systematic study of proton radioactivity of spherical proton emitters within various versions of proximity potential formalisms
In this work we present a systematic study of the proton radioactivity
half-lives of spherical proton emitters within the Coulomb and proximity
potential model. We investigate 28 different versions of the proximity
potential formalisms developed for the description of proton radioactivity,
decay and heavy particle radioactivity. It is found that 21
of them are not suitable to deal with the proton radioactivity, because the
classical turning points cannot be obtained due to the fact
that the depth of the total interaction potential between the emitted proton
and the daughter nucleus is above the proton radioactivity energy. Among the
other 7 versions of the proximity potential formalisms, it is Guo2013 which
gives the lowest rms deviation in the description of the experimental
half-lives of the known spherical proton emitters. We use this proximity
potential formalism to predict the proton radioactivity half-lives of 13
spherical proton emitters, whose proton radioactivity is energetically allowed
or observed but not yet quantified, within a factor of 3.71.Comment: 10 pages, 5 figures. This paper has been accepted by The European
Physical Journal A (in press 2019
Progressive Domain-Independent Feature Decomposition Network for Zero-Shot Sketch-Based Image Retrieval
Zero-shot sketch-based image retrieval (ZS-SBIR) is a specific cross-modal
retrieval task for searching natural images given free-hand sketches under the
zero-shot scenario. Most existing methods solve this problem by simultaneously
projecting visual features and semantic supervision into a low-dimensional
common space for efficient retrieval. However, such low-dimensional projection
destroys the completeness of semantic knowledge in original semantic space, so
that it is unable to transfer useful knowledge well when learning semantic from
different modalities. Moreover, the domain information and semantic information
are entangled in visual features, which is not conducive for cross-modal
matching since it will hinder the reduction of domain gap between sketch and
image. In this paper, we propose a Progressive Domain-independent Feature
Decomposition (PDFD) network for ZS-SBIR. Specifically, with the supervision of
original semantic knowledge, PDFD decomposes visual features into domain
features and semantic ones, and then the semantic features are projected into
common space as retrieval features for ZS-SBIR. The progressive projection
strategy maintains strong semantic supervision. Besides, to guarantee the
retrieval features to capture clean and complete semantic information, the
cross-reconstruction loss is introduced to encourage that any combinations of
retrieval features and domain features can reconstruct the visual features.
Extensive experiments demonstrate the superiority of our PDFD over
state-of-the-art competitors
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