1,170 research outputs found
Slow and steady feature analysis: higher order temporal coherence in video
How can unlabeled video augment visual learning? Existing methods perform
"slow" feature analysis, encouraging the representations of temporally close
frames to exhibit only small differences. While this standard approach captures
the fact that high-level visual signals change slowly over time, it fails to
capture *how* the visual content changes. We propose to generalize slow feature
analysis to "steady" feature analysis. The key idea is to impose a prior that
higher order derivatives in the learned feature space must be small. To this
end, we train a convolutional neural network with a regularizer on tuples of
sequential frames from unlabeled video. It encourages feature changes over time
to be smooth, i.e., similar to the most recent changes. Using five diverse
datasets, including unlabeled YouTube and KITTI videos, we demonstrate our
method's impact on object, scene, and action recognition tasks. We further show
that our features learned from unlabeled video can even surpass a standard
heavily supervised pretraining approach.Comment: in Computer Vision and Pattern Recognition (CVPR) 2016, Las Vegas,
NV, June 201
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
Advances in Hyperspectral Image Classification: Earth monitoring with statistical learning methods
Hyperspectral images show similar statistical properties to natural grayscale
or color photographic images. However, the classification of hyperspectral
images is more challenging because of the very high dimensionality of the
pixels and the small number of labeled examples typically available for
learning. These peculiarities lead to particular signal processing problems,
mainly characterized by indetermination and complex manifolds. The framework of
statistical learning has gained popularity in the last decade. New methods have
been presented to account for the spatial homogeneity of images, to include
user's interaction via active learning, to take advantage of the manifold
structure with semisupervised learning, to extract and encode invariances, or
to adapt classifiers and image representations to unseen yet similar scenes.
This tutuorial reviews the main advances for hyperspectral remote sensing image
classification through illustrative examples.Comment: IEEE Signal Processing Magazine, 201
MetaGCD: Learning to Continually Learn in Generalized Category Discovery
In this paper, we consider a real-world scenario where a model that is
trained on pre-defined classes continually encounters unlabeled data that
contains both known and novel classes. The goal is to continually discover
novel classes while maintaining the performance in known classes. We name the
setting Continual Generalized Category Discovery (C-GCD). Existing methods for
novel class discovery cannot directly handle the C-GCD setting due to some
unrealistic assumptions, such as the unlabeled data only containing novel
classes. Furthermore, they fail to discover novel classes in a continual
fashion. In this work, we lift all these assumptions and propose an approach,
called MetaGCD, to learn how to incrementally discover with less forgetting.
Our proposed method uses a meta-learning framework and leverages the offline
labeled data to simulate the testing incremental learning process. A
meta-objective is defined to revolve around two conflicting learning objectives
to achieve novel class discovery without forgetting. Furthermore, a soft
neighborhood-based contrastive network is proposed to discriminate uncorrelated
images while attracting correlated images. We build strong baselines and
conduct extensive experiments on three widely used benchmarks to demonstrate
the superiority of our method.Comment: This paper has been accepted by ICCV202
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