37,582 research outputs found
DCTM: Discrete-Continuous Transformation Matching for Semantic Flow
Techniques for dense semantic correspondence have provided limited ability to
deal with the geometric variations that commonly exist between semantically
similar images. While variations due to scale and rotation have been examined,
there lack practical solutions for more complex deformations such as affine
transformations because of the tremendous size of the associated solution
space. To address this problem, we present a discrete-continuous transformation
matching (DCTM) framework where dense affine transformation fields are inferred
through a discrete label optimization in which the labels are iteratively
updated via continuous regularization. In this way, our approach draws
solutions from the continuous space of affine transformations in a manner that
can be computed efficiently through constant-time edge-aware filtering and a
proposed affine-varying CNN-based descriptor. Experimental results show that
this model outperforms the state-of-the-art methods for dense semantic
correspondence on various benchmarks
Matching Natural Language Sentences with Hierarchical Sentence Factorization
Semantic matching of natural language sentences or identifying the
relationship between two sentences is a core research problem underlying many
natural language tasks. Depending on whether training data is available, prior
research has proposed both unsupervised distance-based schemes and supervised
deep learning schemes for sentence matching. However, previous approaches
either omit or fail to fully utilize the ordered, hierarchical, and flexible
structures of language objects, as well as the interactions between them. In
this paper, we propose Hierarchical Sentence Factorization---a technique to
factorize a sentence into a hierarchical representation, with the components at
each different scale reordered into a "predicate-argument" form. The proposed
sentence factorization technique leads to the invention of: 1) a new
unsupervised distance metric which calculates the semantic distance between a
pair of text snippets by solving a penalized optimal transport problem while
preserving the logical relationship of words in the reordered sentences, and 2)
new multi-scale deep learning models for supervised semantic training, based on
factorized sentence hierarchies. We apply our techniques to text-pair
similarity estimation and text-pair relationship classification tasks, based on
multiple datasets such as STSbenchmark, the Microsoft Research paraphrase
identification (MSRP) dataset, the SICK dataset, etc. Extensive experiments
show that the proposed hierarchical sentence factorization can be used to
significantly improve the performance of existing unsupervised distance-based
metrics as well as multiple supervised deep learning models based on the
convolutional neural network (CNN) and long short-term memory (LSTM).Comment: Accepted by WWW 2018, 10 page
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