1,457 research outputs found
Learning a Deep Listwise Context Model for Ranking Refinement
Learning to rank has been intensively studied and widely applied in
information retrieval. Typically, a global ranking function is learned from a
set of labeled data, which can achieve good performance on average but may be
suboptimal for individual queries by ignoring the fact that relevant documents
for different queries may have different distributions in the feature space.
Inspired by the idea of pseudo relevance feedback where top ranked documents,
which we refer as the \textit{local ranking context}, can provide important
information about the query's characteristics, we propose to use the inherent
feature distributions of the top results to learn a Deep Listwise Context Model
that helps us fine tune the initial ranked list. Specifically, we employ a
recurrent neural network to sequentially encode the top results using their
feature vectors, learn a local context model and use it to re-rank the top
results. There are three merits with our model: (1) Our model can capture the
local ranking context based on the complex interactions between top results
using a deep neural network; (2) Our model can be built upon existing
learning-to-rank methods by directly using their extracted feature vectors; (3)
Our model is trained with an attention-based loss function, which is more
effective and efficient than many existing listwise methods. Experimental
results show that the proposed model can significantly improve the
state-of-the-art learning to rank methods on benchmark retrieval corpora
Hashing as Tie-Aware Learning to Rank
Hashing, or learning binary embeddings of data, is frequently used in nearest
neighbor retrieval. In this paper, we develop learning to rank formulations for
hashing, aimed at directly optimizing ranking-based evaluation metrics such as
Average Precision (AP) and Normalized Discounted Cumulative Gain (NDCG). We
first observe that the integer-valued Hamming distance often leads to tied
rankings, and propose to use tie-aware versions of AP and NDCG to evaluate
hashing for retrieval. Then, to optimize tie-aware ranking metrics, we derive
their continuous relaxations, and perform gradient-based optimization with deep
neural networks. Our results establish the new state-of-the-art for image
retrieval by Hamming ranking in common benchmarks.Comment: 15 pages, 3 figures. IEEE Conference on Computer Vision and Pattern
Recognition (CVPR), 201
Local Descriptors Optimized for Average Precision
Extraction of local feature descriptors is a vital stage in the solution
pipelines for numerous computer vision tasks. Learning-based approaches improve
performance in certain tasks, but still cannot replace handcrafted features in
general. In this paper, we improve the learning of local feature descriptors by
optimizing the performance of descriptor matching, which is a common stage that
follows descriptor extraction in local feature based pipelines, and can be
formulated as nearest neighbor retrieval. Specifically, we directly optimize a
ranking-based retrieval performance metric, Average Precision, using deep
neural networks. This general-purpose solution can also be viewed as a listwise
learning to rank approach, which is advantageous compared to recent local
ranking approaches. On standard benchmarks, descriptors learned with our
formulation achieve state-of-the-art results in patch verification, patch
retrieval, and image matching.Comment: 13 pages, 8 figures. IEEE Conference on Computer Vision and Pattern
Recognition (CVPR), 201
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