14,264 research outputs found
Optimizing Ranking Measures for Compact Binary Code Learning
Hashing has proven a valuable tool for large-scale information retrieval.
Despite much success, existing hashing methods optimize over simple objectives
such as the reconstruction error or graph Laplacian related loss functions,
instead of the performance evaluation criteria of interest---multivariate
performance measures such as the AUC and NDCG. Here we present a general
framework (termed StructHash) that allows one to directly optimize multivariate
performance measures. The resulting optimization problem can involve
exponentially or infinitely many variables and constraints, which is more
challenging than standard structured output learning. To solve the StructHash
optimization problem, we use a combination of column generation and
cutting-plane techniques. We demonstrate the generality of StructHash by
applying it to ranking prediction and image retrieval, and show that it
outperforms a few state-of-the-art hashing methods.Comment: Appearing in Proc. European Conference on Computer Vision 201
A General Two-Step Approach to Learning-Based Hashing
Most existing approaches to hashing apply a single form of hash function, and
an optimization process which is typically deeply coupled to this specific
form. This tight coupling restricts the flexibility of the method to respond to
the data, and can result in complex optimization problems that are difficult to
solve. Here we propose a flexible yet simple framework that is able to
accommodate different types of loss functions and hash functions. This
framework allows a number of existing approaches to hashing to be placed in
context, and simplifies the development of new problem-specific hashing
methods. Our framework decomposes hashing learning problem into two steps: hash
bit learning and hash function learning based on the learned bits. The first
step can typically be formulated as binary quadratic problems, and the second
step can be accomplished by training standard binary classifiers. Both problems
have been extensively studied in the literature. Our extensive experiments
demonstrate that the proposed framework is effective, flexible and outperforms
the state-of-the-art.Comment: 13 pages. Appearing in Int. Conf. Computer Vision (ICCV) 201
Format Abstraction for Sparse Tensor Algebra Compilers
This paper shows how to build a sparse tensor algebra compiler that is
agnostic to tensor formats (data layouts). We develop an interface that
describes formats in terms of their capabilities and properties, and show how
to build a modular code generator where new formats can be added as plugins. We
then describe six implementations of the interface that compose to form the
dense, CSR/CSF, COO, DIA, ELL, and HASH tensor formats and countless variants
thereof. With these implementations at hand, our code generator can generate
code to compute any tensor algebra expression on any combination of the
aforementioned formats.
To demonstrate our technique, we have implemented it in the taco tensor
algebra compiler. Our modular code generator design makes it simple to add
support for new tensor formats, and the performance of the generated code is
competitive with hand-optimized implementations. Furthermore, by extending taco
to support a wider range of formats specialized for different application and
data characteristics, we can improve end-user application performance. For
example, if input data is provided in the COO format, our technique allows
computing a single matrix-vector multiplication directly with the data in COO,
which is up to 3.6 faster than by first converting the data to CSR.Comment: Presented at OOPSLA 201
Region-DH: Region-based Deep Hashing for Multi-Instance Aware Image Retrieval
This paper introduces an instance-aware hashing approach Region-DH for large-scale multi-label image retrieval. The accurate object bounds can significantly increase the hashing performance of instance features. We design a unified deep neural network that simultaneously localizes and recognizes objects while learning the hash functions for binary codes. Region-DH focuses on recognizing objects and building compact binary codes that represent more foreground patterns. Region-DH can flexibly be used with existing deep neural networks or more complex object detectors for image hashing. Extensive experiments are performed on benchmark datasets and show the efficacy and robustness of the proposed Region-DH model
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