23,755 research outputs found
Deep Sketch Hashing: Fast Free-hand Sketch-Based Image Retrieval
Free-hand sketch-based image retrieval (SBIR) is a specific cross-view
retrieval task, in which queries are abstract and ambiguous sketches while the
retrieval database is formed with natural images. Work in this area mainly
focuses on extracting representative and shared features for sketches and
natural images. However, these can neither cope well with the geometric
distortion between sketches and images nor be feasible for large-scale SBIR due
to the heavy continuous-valued distance computation. In this paper, we speed up
SBIR by introducing a novel binary coding method, named \textbf{Deep Sketch
Hashing} (DSH), where a semi-heterogeneous deep architecture is proposed and
incorporated into an end-to-end binary coding framework. Specifically, three
convolutional neural networks are utilized to encode free-hand sketches,
natural images and, especially, the auxiliary sketch-tokens which are adopted
as bridges to mitigate the sketch-image geometric distortion. The learned DSH
codes can effectively capture the cross-view similarities as well as the
intrinsic semantic correlations between different categories. To the best of
our knowledge, DSH is the first hashing work specifically designed for
category-level SBIR with an end-to-end deep architecture. The proposed DSH is
comprehensively evaluated on two large-scale datasets of TU-Berlin Extension
and Sketchy, and the experiments consistently show DSH's superior SBIR
accuracies over several state-of-the-art methods, while achieving significantly
reduced retrieval time and memory footprint.Comment: This paper will appear as a spotlight paper in CVPR201
Universal Indexes for Highly Repetitive Document Collections
Indexing highly repetitive collections has become a relevant problem with the
emergence of large repositories of versioned documents, among other
applications. These collections may reach huge sizes, but are formed mostly of
documents that are near-copies of others. Traditional techniques for indexing
these collections fail to properly exploit their regularities in order to
reduce space.
We introduce new techniques for compressing inverted indexes that exploit
this near-copy regularity. They are based on run-length, Lempel-Ziv, or grammar
compression of the differential inverted lists, instead of the usual practice
of gap-encoding them. We show that, in this highly repetitive setting, our
compression methods significantly reduce the space obtained with classical
techniques, at the price of moderate slowdowns. Moreover, our best methods are
universal, that is, they do not need to know the versioning structure of the
collection, nor that a clear versioning structure even exists.
We also introduce compressed self-indexes in the comparison. These are
designed for general strings (not only natural language texts) and represent
the text collection plus the index structure (not an inverted index) in
integrated form. We show that these techniques can compress much further, using
a small fraction of the space required by our new inverted indexes. Yet, they
are orders of magnitude slower.Comment: This research has received funding from the European Union's Horizon
2020 research and innovation programme under the Marie Sk{\l}odowska-Curie
Actions H2020-MSCA-RISE-2015 BIRDS GA No. 69094
Optimally adapted multi-state neural networks trained with noise
The principle of adaptation in a noisy retrieval environment is extended here
to a diluted attractor neural network of Q-state neurons trained with noisy
data. The network is adapted to an appropriate noisy training overlap and
training activity which are determined self-consistently by the optimized
retrieval attractor overlap and activity. The optimized storage capacity and
the corresponding retriever overlap are considerably enhanced by an adequate
threshold in the states. Explicit results for improved optimal performance and
new retriever phase diagrams are obtained for Q=3 and Q=4, with coexisting
phases over a wide range of thresholds. Most of the interesting results are
stable to replica-symmetry-breaking fluctuations.Comment: 22 pages, 5 figures, accepted for publication in PR
Query Resolution for Conversational Search with Limited Supervision
In this work we focus on multi-turn passage retrieval as a crucial component
of conversational search. One of the key challenges in multi-turn passage
retrieval comes from the fact that the current turn query is often
underspecified due to zero anaphora, topic change, or topic return. Context
from the conversational history can be used to arrive at a better expression of
the current turn query, defined as the task of query resolution. In this paper,
we model the query resolution task as a binary term classification problem: for
each term appearing in the previous turns of the conversation decide whether to
add it to the current turn query or not. We propose QuReTeC (Query Resolution
by Term Classification), a neural query resolution model based on bidirectional
transformers. We propose a distant supervision method to automatically generate
training data by using query-passage relevance labels. Such labels are often
readily available in a collection either as human annotations or inferred from
user interactions. We show that QuReTeC outperforms state-of-the-art models,
and furthermore, that our distant supervision method can be used to
substantially reduce the amount of human-curated data required to train
QuReTeC. We incorporate QuReTeC in a multi-turn, multi-stage passage retrieval
architecture and demonstrate its effectiveness on the TREC CAsT dataset.Comment: SIGIR 2020 full conference pape
On the Use of Suffix Arrays for Memory-Efficient Lempel-Ziv Data Compression
Much research has been devoted to optimizing algorithms of the Lempel-Ziv
(LZ) 77 family, both in terms of speed and memory requirements. Binary search
trees and suffix trees (ST) are data structures that have been often used for
this purpose, as they allow fast searches at the expense of memory usage.
In recent years, there has been interest on suffix arrays (SA), due to their
simplicity and low memory requirements. One key issue is that an SA can solve
the sub-string problem almost as efficiently as an ST, using less memory. This
paper proposes two new SA-based algorithms for LZ encoding, which require no
modifications on the decoder side. Experimental results on standard benchmarks
show that our algorithms, though not faster, use 3 to 5 times less memory than
the ST counterparts. Another important feature of our SA-based algorithms is
that the amount of memory is independent of the text to search, thus the memory
that has to be allocated can be defined a priori. These features of low and
predictable memory requirements are of the utmost importance in several
scenarios, such as embedded systems, where memory is at a premium and speed is
not critical. Finally, we point out that the new algorithms are general, in the
sense that they are adequate for applications other than LZ compression, such
as text retrieval and forward/backward sub-string search.Comment: 10 pages, submited to IEEE - Data Compression Conference 200
An image retrieval system based on explicit and implicit feedback on a tablet computer
Our research aims at developing a image retrieval system which uses relevance feedback to build a hybrid search /recommendation system for images according to users’ inter ests. An image retrieval application running on a tablet computer gathers explicit feedback through the touchscreen but also uses multiple sensing technologies to gather implicit feedback such as emotion and action. A recommendation mechanism driven by collaborative filtering is implemented to verify our interaction design
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