6,828 research outputs found
From Word to Sense Embeddings: A Survey on Vector Representations of Meaning
Over the past years, distributed semantic representations have proved to be
effective and flexible keepers of prior knowledge to be integrated into
downstream applications. This survey focuses on the representation of meaning.
We start from the theoretical background behind word vector space models and
highlight one of their major limitations: the meaning conflation deficiency,
which arises from representing a word with all its possible meanings as a
single vector. Then, we explain how this deficiency can be addressed through a
transition from the word level to the more fine-grained level of word senses
(in its broader acceptation) as a method for modelling unambiguous lexical
meaning. We present a comprehensive overview of the wide range of techniques in
the two main branches of sense representation, i.e., unsupervised and
knowledge-based. Finally, this survey covers the main evaluation procedures and
applications for this type of representation, and provides an analysis of four
of its important aspects: interpretability, sense granularity, adaptability to
different domains and compositionality.Comment: 46 pages, 8 figures. Published in Journal of Artificial Intelligence
Researc
Learning a Disentangled Embedding for Monocular 3D Shape Retrieval and Pose Estimation
We propose a novel approach to jointly perform 3D shape retrieval and pose
estimation from monocular images.In order to make the method robust to
real-world image variations, e.g. complex textures and backgrounds, we learn an
embedding space from 3D data that only includes the relevant information,
namely the shape and pose. Our approach explicitly disentangles a shape vector
and a pose vector, which alleviates both pose bias for 3D shape retrieval and
categorical bias for pose estimation. We then train a CNN to map the images to
this embedding space, and then retrieve the closest 3D shape from the database
and estimate the 6D pose of the object. Our method achieves 10.3 median error
for pose estimation and 0.592 top-1-accuracy for category agnostic 3D object
retrieval on the Pascal3D+ dataset, outperforming the previous state-of-the-art
methods on both tasks
Recent Advances in Transfer Learning for Cross-Dataset Visual Recognition: A Problem-Oriented Perspective
This paper takes a problem-oriented perspective and presents a comprehensive
review of transfer learning methods, both shallow and deep, for cross-dataset
visual recognition. Specifically, it categorises the cross-dataset recognition
into seventeen problems based on a set of carefully chosen data and label
attributes. Such a problem-oriented taxonomy has allowed us to examine how
different transfer learning approaches tackle each problem and how well each
problem has been researched to date. The comprehensive problem-oriented review
of the advances in transfer learning with respect to the problem has not only
revealed the challenges in transfer learning for visual recognition, but also
the problems (e.g. eight of the seventeen problems) that have been scarcely
studied. This survey not only presents an up-to-date technical review for
researchers, but also a systematic approach and a reference for a machine
learning practitioner to categorise a real problem and to look up for a
possible solution accordingly
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