17,100 research outputs found
Learning to Recognize Actions from Limited Training Examples Using a Recurrent Spiking Neural Model
A fundamental challenge in machine learning today is to build a model that
can learn from few examples. Here, we describe a reservoir based spiking neural
model for learning to recognize actions with a limited number of labeled
videos. First, we propose a novel encoding, inspired by how microsaccades
influence visual perception, to extract spike information from raw video data
while preserving the temporal correlation across different frames. Using this
encoding, we show that the reservoir generalizes its rich dynamical activity
toward signature action/movements enabling it to learn from few training
examples. We evaluate our approach on the UCF-101 dataset. Our experiments
demonstrate that our proposed reservoir achieves 81.3%/87% Top-1/Top-5
accuracy, respectively, on the 101-class data while requiring just 8 video
examples per class for training. Our results establish a new benchmark for
action recognition from limited video examples for spiking neural models while
yielding competetive accuracy with respect to state-of-the-art non-spiking
neural models.Comment: 13 figures (includes supplementary information
On human motion prediction using recurrent neural networks
Human motion modelling is a classical problem at the intersection of graphics
and computer vision, with applications spanning human-computer interaction,
motion synthesis, and motion prediction for virtual and augmented reality.
Following the success of deep learning methods in several computer vision
tasks, recent work has focused on using deep recurrent neural networks (RNNs)
to model human motion, with the goal of learning time-dependent representations
that perform tasks such as short-term motion prediction and long-term human
motion synthesis. We examine recent work, with a focus on the evaluation
methodologies commonly used in the literature, and show that, surprisingly,
state-of-the-art performance can be achieved by a simple baseline that does not
attempt to model motion at all. We investigate this result, and analyze recent
RNN methods by looking at the architectures, loss functions, and training
procedures used in state-of-the-art approaches. We propose three changes to the
standard RNN models typically used for human motion, which result in a simple
and scalable RNN architecture that obtains state-of-the-art performance on
human motion prediction.Comment: Accepted at CVPR 1
End-to-end Learning of Driving Models from Large-scale Video Datasets
Robust perception-action models should be learned from training data with
diverse visual appearances and realistic behaviors, yet current approaches to
deep visuomotor policy learning have been generally limited to in-situ models
learned from a single vehicle or a simulation environment. We advocate learning
a generic vehicle motion model from large scale crowd-sourced video data, and
develop an end-to-end trainable architecture for learning to predict a
distribution over future vehicle egomotion from instantaneous monocular camera
observations and previous vehicle state. Our model incorporates a novel
FCN-LSTM architecture, which can be learned from large-scale crowd-sourced
vehicle action data, and leverages available scene segmentation side tasks to
improve performance under a privileged learning paradigm.Comment: camera ready for CVPR201
Seeing into Darkness: Scotopic Visual Recognition
Images are formed by counting how many photons traveling from a given set of
directions hit an image sensor during a given time interval. When photons are
few and far in between, the concept of `image' breaks down and it is best to
consider directly the flow of photons. Computer vision in this regime, which we
call `scotopic', is radically different from the classical image-based paradigm
in that visual computations (classification, control, search) have to take
place while the stream of photons is captured and decisions may be taken as
soon as enough information is available. The scotopic regime is important for
biomedical imaging, security, astronomy and many other fields. Here we develop
a framework that allows a machine to classify objects with as few photons as
possible, while maintaining the error rate below an acceptable threshold. A
dynamic and asymptotically optimal speed-accuracy tradeoff is a key feature of
this framework. We propose and study an algorithm to optimize the tradeoff of a
convolutional network directly from lowlight images and evaluate on simulated
images from standard datasets. Surprisingly, scotopic systems can achieve
comparable classification performance as traditional vision systems while using
less than 0.1% of the photons in a conventional image. In addition, we
demonstrate that our algorithms work even when the illuminance of the
environment is unknown and varying. Last, we outline a spiking neural network
coupled with photon-counting sensors as a power-efficient hardware realization
of scotopic algorithms.Comment: 23 pages, 6 figure
Beyond Short Snippets: Deep Networks for Video Classification
Convolutional neural networks (CNNs) have been extensively applied for image
recognition problems giving state-of-the-art results on recognition, detection,
segmentation and retrieval. In this work we propose and evaluate several deep
neural network architectures to combine image information across a video over
longer time periods than previously attempted. We propose two methods capable
of handling full length videos. The first method explores various convolutional
temporal feature pooling architectures, examining the various design choices
which need to be made when adapting a CNN for this task. The second proposed
method explicitly models the video as an ordered sequence of frames. For this
purpose we employ a recurrent neural network that uses Long Short-Term Memory
(LSTM) cells which are connected to the output of the underlying CNN. Our best
networks exhibit significant performance improvements over previously published
results on the Sports 1 million dataset (73.1% vs. 60.9%) and the UCF-101
datasets with (88.6% vs. 88.0%) and without additional optical flow information
(82.6% vs. 72.8%)
Lifelong Learning of Spatiotemporal Representations with Dual-Memory Recurrent Self-Organization
Artificial autonomous agents and robots interacting in complex environments
are required to continually acquire and fine-tune knowledge over sustained
periods of time. The ability to learn from continuous streams of information is
referred to as lifelong learning and represents a long-standing challenge for
neural network models due to catastrophic forgetting. Computational models of
lifelong learning typically alleviate catastrophic forgetting in experimental
scenarios with given datasets of static images and limited complexity, thereby
differing significantly from the conditions artificial agents are exposed to.
In more natural settings, sequential information may become progressively
available over time and access to previous experience may be restricted. In
this paper, we propose a dual-memory self-organizing architecture for lifelong
learning scenarios. The architecture comprises two growing recurrent networks
with the complementary tasks of learning object instances (episodic memory) and
categories (semantic memory). Both growing networks can expand in response to
novel sensory experience: the episodic memory learns fine-grained
spatiotemporal representations of object instances in an unsupervised fashion
while the semantic memory uses task-relevant signals to regulate structural
plasticity levels and develop more compact representations from episodic
experience. For the consolidation of knowledge in the absence of external
sensory input, the episodic memory periodically replays trajectories of neural
reactivations. We evaluate the proposed model on the CORe50 benchmark dataset
for continuous object recognition, showing that we significantly outperform
current methods of lifelong learning in three different incremental learning
scenario
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