204 research outputs found
Massively Parallel Video Networks
We introduce a class of causal video understanding models that aims to
improve efficiency of video processing by maximising throughput, minimising
latency, and reducing the number of clock cycles. Leveraging operation
pipelining and multi-rate clocks, these models perform a minimal amount of
computation (e.g. as few as four convolutional layers) for each frame per
timestep to produce an output. The models are still very deep, with dozens of
such operations being performed but in a pipelined fashion that enables
depth-parallel computation. We illustrate the proposed principles by applying
them to existing image architectures and analyse their behaviour on two video
tasks: action recognition and human keypoint localisation. The results show
that a significant degree of parallelism, and implicitly speedup, can be
achieved with little loss in performance.Comment: Fixed typos in densenet model definition in appendi
ICNet for Real-Time Semantic Segmentation on High-Resolution Images
We focus on the challenging task of real-time semantic segmentation in this
paper. It finds many practical applications and yet is with fundamental
difficulty of reducing a large portion of computation for pixel-wise label
inference. We propose an image cascade network (ICNet) that incorporates
multi-resolution branches under proper label guidance to address this
challenge. We provide in-depth analysis of our framework and introduce the
cascade feature fusion unit to quickly achieve high-quality segmentation. Our
system yields real-time inference on a single GPU card with decent quality
results evaluated on challenging datasets like Cityscapes, CamVid and
COCO-Stuff.Comment: ECCV 201
Object Contour and Edge Detection with RefineContourNet
A ResNet-based multi-path refinement CNN is used for object contour
detection. For this task, we prioritise the effective utilization of the
high-level abstraction capability of a ResNet, which leads to state-of-the-art
results for edge detection. Keeping our focus in mind, we fuse the high, mid
and low-level features in that specific order, which differs from many other
approaches. It uses the tensor with the highest-levelled features as the
starting point to combine it layer-by-layer with features of a lower
abstraction level until it reaches the lowest level. We train this network on a
modified PASCAL VOC 2012 dataset for object contour detection and evaluate on a
refined PASCAL-val dataset reaching an excellent performance and an Optimal
Dataset Scale (ODS) of 0.752. Furthermore, by fine-training on the BSDS500
dataset we reach state-of-the-art results for edge-detection with an ODS of
0.824.Comment: Keywords: Object Contour Detection, Edge Detection, Multi-Path
Refinement CN
A Review of Object Detection Models based on Convolutional Neural Network
Convolutional Neural Network (CNN) has become the state-of-the-art for object
detection in image task. In this chapter, we have explained different
state-of-the-art CNN based object detection models. We have made this review
with categorization those detection models according to two different
approaches: two-stage approach and one-stage approach. Through this chapter, it
has shown advancements in object detection models from R-CNN to latest
RefineDet. It has also discussed the model description and training details of
each model. Here, we have also drawn a comparison among those models.Comment: 17 pages, 11 figures, 1 tabl
Tversky loss function for image segmentation using 3D fully convolutional deep networks
Fully convolutional deep neural networks carry out excellent potential for
fast and accurate image segmentation. One of the main challenges in training
these networks is data imbalance, which is particularly problematic in medical
imaging applications such as lesion segmentation where the number of lesion
voxels is often much lower than the number of non-lesion voxels. Training with
unbalanced data can lead to predictions that are severely biased towards high
precision but low recall (sensitivity), which is undesired especially in
medical applications where false negatives are much less tolerable than false
positives. Several methods have been proposed to deal with this problem
including balanced sampling, two step training, sample re-weighting, and
similarity loss functions. In this paper, we propose a generalized loss
function based on the Tversky index to address the issue of data imbalance and
achieve much better trade-off between precision and recall in training 3D fully
convolutional deep neural networks. Experimental results in multiple sclerosis
lesion segmentation on magnetic resonance images show improved F2 score, Dice
coefficient, and the area under the precision-recall curve in test data. Based
on these results we suggest Tversky loss function as a generalized framework to
effectively train deep neural networks
Self-Supervised Relative Depth Learning for Urban Scene Understanding
As an agent moves through the world, the apparent motion of scene elements is
(usually) inversely proportional to their depth. It is natural for a learning
agent to associate image patterns with the magnitude of their displacement over
time: as the agent moves, faraway mountains don't move much; nearby trees move
a lot. This natural relationship between the appearance of objects and their
motion is a rich source of information about the world. In this work, we start
by training a deep network, using fully automatic supervision, to predict
relative scene depth from single images. The relative depth training images are
automatically derived from simple videos of cars moving through a scene, using
recent motion segmentation techniques, and no human-provided labels. This proxy
task of predicting relative depth from a single image induces features in the
network that result in large improvements in a set of downstream tasks
including semantic segmentation, joint road segmentation and car detection, and
monocular (absolute) depth estimation, over a network trained from scratch. The
improvement on the semantic segmentation task is greater than those produced by
any other automatically supervised methods. Moreover, for monocular depth
estimation, our unsupervised pre-training method even outperforms supervised
pre-training with ImageNet. In addition, we demonstrate benefits from learning
to predict (unsupervised) relative depth in the specific videos associated with
various downstream tasks. We adapt to the specific scenes in those tasks in an
unsupervised manner to improve performance. In summary, for semantic
segmentation, we present state-of-the-art results among methods that do not use
supervised pre-training, and we even exceed the performance of supervised
ImageNet pre-trained models for monocular depth estimation, achieving results
that are comparable with state-of-the-art methods
Survey on Vision-based Path Prediction
Path prediction is a fundamental task for estimating how pedestrians or
vehicles are going to move in a scene. Because path prediction as a task of
computer vision uses video as input, various information used for prediction,
such as the environment surrounding the target and the internal state of the
target, need to be estimated from the video in addition to predicting paths.
Many prediction approaches that include understanding the environment and the
internal state have been proposed. In this survey, we systematically summarize
methods of path prediction that take video as input and and extract features
from the video. Moreover, we introduce datasets used to evaluate path
prediction methods quantitatively.Comment: DAPI 201
CSC-GAN:Cycle and Semantic Consistency for Dataset Augmentation
Image-to-image translation is a computer vision problem where a task learns a mapping from a source domain A to a target domain B using a training set. However, this translation is not always accurate, and during the translation process, relevant semantic information can deteriorate. To handle this problem, we propose a new cycle-consistent, adversarially trained image-to-image translation with a loss function that is constrained by semantic segmentation. This formulation encourages the model to preserve semantic information during the translation process. For this purpose, our loss function evaluates the accuracy of the synthetically generated image against a semantic segmentation model, previously trained. Reported results show that our proposed method can significantly increase the level of details in the synthetic images. We further demonstrate our method’s effectiveness by applying it as a dataset augmentation technique, for a minimal dataset, showing that it can improve the semantic segmentation accuracy
AutoSimulate: (Quickly) Learning Synthetic Data Generation
Simulation is increasingly being used for generating large labelled datasets
in many machine learning problems. Recent methods have focused on adjusting
simulator parameters with the goal of maximising accuracy on a validation task,
usually relying on REINFORCE-like gradient estimators. However these approaches
are very expensive as they treat the entire data generation, model training,
and validation pipeline as a black-box and require multiple costly objective
evaluations at each iteration. We propose an efficient alternative for optimal
synthetic data generation, based on a novel differentiable approximation of the
objective. This allows us to optimize the simulator, which may be
non-differentiable, requiring only one objective evaluation at each iteration
with a little overhead. We demonstrate on a state-of-the-art photorealistic
renderer that the proposed method finds the optimal data distribution faster
(up to ), with significantly reduced training data generation (up to
) and better accuracy () on real-world test datasets than
previous methods.Comment: ECCV 202
The Twins Study: NASA's First Foray into 21st Century Omics Research
The full array of 21st century omics-based research methods should be intelligently employed to reduce the health and performance risks that astronauts will be exposed to during exploration missions beyond low Earth Orbit. In March of 2015, US Astronaut Scott Kelly will launch to the International Space Station for a one year mission while his twin brother, Mark Kelly, a retired US Astronaut, remains on the ground. This situation presents an extremely rare flight opportunity to perform an integrated omics-based demonstration pilot study involving identical twin astronauts. A group of 10 principal investigators has been competitively selected, funded, and teamed together to form the Twins Study. A very broad range of biological function are being examined including the genome, epigenome, transcriptome, proteome, metabolome, gut microbiome, immunological response to vaccinations, indicators of atherosclerosis, physiological fluid shifts, and cognition. The plans for the Twins Study and an overview of initial results will be described as well as the technological and ethical issues raised for such spaceflight studies. An anticipated outcome of the Twins Study is that it will place NASA on a trajectory of using omics-based information to develop precision countermeasures for individual astronauts
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