45 research outputs found
Ultrafast Video Attention Prediction with Coupled Knowledge Distillation
Large convolutional neural network models have recently demonstrated
impressive performance on video attention prediction. Conventionally, these
models are with intensive computation and large memory. To address these
issues, we design an extremely light-weight network with ultrafast speed, named
UVA-Net. The network is constructed based on depth-wise convolutions and takes
low-resolution images as input. However, this straight-forward acceleration
method will decrease performance dramatically. To this end, we propose a
coupled knowledge distillation strategy to augment and train the network
effectively. With this strategy, the model can further automatically discover
and emphasize implicit useful cues contained in the data. Both spatial and
temporal knowledge learned by the high-resolution complex teacher networks also
can be distilled and transferred into the proposed low-resolution light-weight
spatiotemporal network. Experimental results show that the performance of our
model is comparable to ten state-of-the-art models in video attention
prediction, while it costs only 0.68 MB memory footprint, runs about 10,106 FPS
on GPU and 404 FPS on CPU, which is 206 times faster than previous models
Very High Resolution (VHR) Satellite Imagery: Processing and Applications
Recently, growing interest in the use of remote sensing imagery has appeared to provide synoptic maps of water quality parameters in coastal and inner water ecosystems;, monitoring of complex land ecosystems for biodiversity conservation; precision agriculture for the management of soils, crops, and pests; urban planning; disaster monitoring, etc. However, for these maps to achieve their full potential, it is important to engage in periodic monitoring and analysis of multi-temporal changes. In this context, very high resolution (VHR) satellite-based optical, infrared, and radar imaging instruments provide reliable information to implement spatially-based conservation actions. Moreover, they enable observations of parameters of our environment at greater broader spatial and finer temporal scales than those allowed through field observation alone. In this sense, recent very high resolution satellite technologies and image processing algorithms present the opportunity to develop quantitative techniques that have the potential to improve upon traditional techniques in terms of cost, mapping fidelity, and objectivity. Typical applications include multi-temporal classification, recognition and tracking of specific patterns, multisensor data fusion, analysis of land/marine ecosystem processes and environment monitoring, etc. This book aims to collect new developments, methodologies, and applications of very high resolution satellite data for remote sensing. The works selected provide to the research community the most recent advances on all aspects of VHR satellite remote sensing
Visually Adversarial Attacks and Defenses in the Physical World: A Survey
Although Deep Neural Networks (DNNs) have been widely applied in various
real-world scenarios, they are vulnerable to adversarial examples. The current
adversarial attacks in computer vision can be divided into digital attacks and
physical attacks according to their different attack forms. Compared with
digital attacks, which generate perturbations in the digital pixels, physical
attacks are more practical in the real world. Owing to the serious security
problem caused by physically adversarial examples, many works have been
proposed to evaluate the physically adversarial robustness of DNNs in the past
years. In this paper, we summarize a survey versus the current physically
adversarial attacks and physically adversarial defenses in computer vision. To
establish a taxonomy, we organize the current physical attacks from attack
tasks, attack forms, and attack methods, respectively. Thus, readers can have a
systematic knowledge of this topic from different aspects. For the physical
defenses, we establish the taxonomy from pre-processing, in-processing, and
post-processing for the DNN models to achieve full coverage of the adversarial
defenses. Based on the above survey, we finally discuss the challenges of this
research field and further outlook on the future direction