39,939 research outputs found
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
Balanced Quantization: An Effective and Efficient Approach to Quantized Neural Networks
Quantized Neural Networks (QNNs), which use low bitwidth numbers for
representing parameters and performing computations, have been proposed to
reduce the computation complexity, storage size and memory usage. In QNNs,
parameters and activations are uniformly quantized, such that the
multiplications and additions can be accelerated by bitwise operations.
However, distributions of parameters in Neural Networks are often imbalanced,
such that the uniform quantization determined from extremal values may under
utilize available bitwidth. In this paper, we propose a novel quantization
method that can ensure the balance of distributions of quantized values. Our
method first recursively partitions the parameters by percentiles into balanced
bins, and then applies uniform quantization. We also introduce computationally
cheaper approximations of percentiles to reduce the computation overhead
introduced. Overall, our method improves the prediction accuracies of QNNs
without introducing extra computation during inference, has negligible impact
on training speed, and is applicable to both Convolutional Neural Networks and
Recurrent Neural Networks. Experiments on standard datasets including ImageNet
and Penn Treebank confirm the effectiveness of our method. On ImageNet, the
top-5 error rate of our 4-bit quantized GoogLeNet model is 12.7\%, which is
superior to the state-of-the-arts of QNNs
Non Parametric Distributed Inference in Sensor Networks Using Box Particles Messages
This paper deals with the problem of inference in distributed systems where the probability model is stored in a distributed fashion. Graphical models provide powerful tools for modeling this kind of problems. Inspired by the box particle filter which combines interval analysis with particle filtering to solve temporal inference problems, this paper introduces a belief propagation-like message-passing algorithm that uses bounded error methods to solve the inference problem defined on an arbitrary graphical model. We show the theoretic derivation of the novel algorithm and we test its performance on the problem of calibration in wireless sensor networks. That is the positioning of a number of randomly deployed sensors, according to some reference defined by a set of anchor nodes for which the positions are known a priori. The new algorithm, while achieving a better or similar performance, offers impressive reduction of the information circulating in the network and the needed computation times
Dynamic Face Video Segmentation via Reinforcement Learning
For real-time semantic video segmentation, most recent works utilised a
dynamic framework with a key scheduler to make online key/non-key decisions.
Some works used a fixed key scheduling policy, while others proposed adaptive
key scheduling methods based on heuristic strategies, both of which may lead to
suboptimal global performance. To overcome this limitation, we model the online
key decision process in dynamic video segmentation as a deep reinforcement
learning problem and learn an efficient and effective scheduling policy from
expert information about decision history and from the process of maximising
global return. Moreover, we study the application of dynamic video segmentation
on face videos, a field that has not been investigated before. By evaluating on
the 300VW dataset, we show that the performance of our reinforcement key
scheduler outperforms that of various baselines in terms of both effective key
selections and running speed. Further results on the Cityscapes dataset
demonstrate that our proposed method can also generalise to other scenarios. To
the best of our knowledge, this is the first work to use reinforcement learning
for online key-frame decision in dynamic video segmentation, and also the first
work on its application on face videos.Comment: CVPR 2020. 300VW with segmentation labels is available at:
https://github.com/mapleandfire/300VW-Mas
Why Use Sobolev Metrics on the Space of Curves
We study reparametrization invariant Sobolev metrics on spaces of regular curves. We discuss their completeness properties and the resulting usability for applications in shape analysis. In particular, we will argue, that the development of efficient numerical methods for higher order Sobolev type metrics is an extremely desirable goal
- …