6,378 research outputs found
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
Memory Based Online Learning of Deep Representations from Video Streams
We present a novel online unsupervised method for face identity learning from
video streams. The method exploits deep face descriptors together with a memory
based learning mechanism that takes advantage of the temporal coherence of
visual data. Specifically, we introduce a discriminative feature matching
solution based on Reverse Nearest Neighbour and a feature forgetting strategy
that detect redundant features and discard them appropriately while time
progresses. It is shown that the proposed learning procedure is asymptotically
stable and can be effectively used in relevant applications like multiple face
identification and tracking from unconstrained video streams. Experimental
results show that the proposed method achieves comparable results in the task
of multiple face tracking and better performance in face identification with
offline approaches exploiting future information. Code will be publicly
available.Comment: arXiv admin note: text overlap with arXiv:1708.0361
Programmable Spectrometry -- Per-pixel Classification of Materials using Learned Spectral Filters
Many materials have distinct spectral profiles. This facilitates estimation
of the material composition of a scene at each pixel by first acquiring its
hyperspectral image, and subsequently filtering it using a bank of spectral
profiles. This process is inherently wasteful since only a set of linear
projections of the acquired measurements contribute to the classification task.
We propose a novel programmable camera that is capable of producing images of a
scene with an arbitrary spectral filter. We use this camera to optically
implement the spectral filtering of the scene's hyperspectral image with the
bank of spectral profiles needed to perform per-pixel material classification.
This provides gains both in terms of acquisition speed --- since only the
relevant measurements are acquired --- and in signal-to-noise ratio --- since
we invariably avoid narrowband filters that are light inefficient. Given
training data, we use a range of classical and modern techniques including SVMs
and neural networks to identify the bank of spectral profiles that facilitate
material classification. We verify the method in simulations on standard
datasets as well as real data using a lab prototype of the camera
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