44,185 research outputs found
Unsupervised learning of clutter-resistant visual representations from natural videos
Populations of neurons in inferotemporal cortex (IT) maintain an explicit
code for object identity that also tolerates transformations of object
appearance e.g., position, scale, viewing angle [1, 2, 3]. Though the learning
rules are not known, recent results [4, 5, 6] suggest the operation of an
unsupervised temporal-association-based method e.g., Foldiak's trace rule [7].
Such methods exploit the temporal continuity of the visual world by assuming
that visual experience over short timescales will tend to have invariant
identity content. Thus, by associating representations of frames from nearby
times, a representation that tolerates whatever transformations occurred in the
video may be achieved. Many previous studies verified that such rules can work
in simple situations without background clutter, but the presence of visual
clutter has remained problematic for this approach. Here we show that temporal
association based on large class-specific filters (templates) avoids the
problem of clutter. Our system learns in an unsupervised way from natural
videos gathered from the internet, and is able to perform a difficult
unconstrained face recognition task on natural images: Labeled Faces in the
Wild [8]
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
Learning Temporal Transformations From Time-Lapse Videos
Based on life-long observations of physical, chemical, and biologic phenomena
in the natural world, humans can often easily picture in their minds what an
object will look like in the future. But, what about computers? In this paper,
we learn computational models of object transformations from time-lapse videos.
In particular, we explore the use of generative models to create depictions of
objects at future times. These models explore several different prediction
tasks: generating a future state given a single depiction of an object,
generating a future state given two depictions of an object at different times,
and generating future states recursively in a recurrent framework. We provide
both qualitative and quantitative evaluations of the generated results, and
also conduct a human evaluation to compare variations of our models.Comment: ECCV201
Ambient Sound Provides Supervision for Visual Learning
The sound of crashing waves, the roar of fast-moving cars -- sound conveys
important information about the objects in our surroundings. In this work, we
show that ambient sounds can be used as a supervisory signal for learning
visual models. To demonstrate this, we train a convolutional neural network to
predict a statistical summary of the sound associated with a video frame. We
show that, through this process, the network learns a representation that
conveys information about objects and scenes. We evaluate this representation
on several recognition tasks, finding that its performance is comparable to
that of other state-of-the-art unsupervised learning methods. Finally, we show
through visualizations that the network learns units that are selective to
objects that are often associated with characteristic sounds.Comment: ECCV 201
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