4,694 research outputs found
R-CNN minus R
Deep convolutional neural networks (CNNs) have had a major impact in most
areas of image understanding, including object category detection. In object
detection, methods such as R-CNN have obtained excellent results by integrating
CNNs with region proposal generation algorithms such as selective search. In
this paper, we investigate the role of proposal generation in CNN-based
detectors in order to determine whether it is a necessary modelling component,
carrying essential geometric information not contained in the CNN, or whether
it is merely a way of accelerating detection. We do so by designing and
evaluating a detector that uses a trivial region generation scheme, constant
for each image. Combined with SPP, this results in an excellent and fast
detector that does not require to process an image with algorithms other than
the CNN itself. We also streamline and simplify the training of CNN-based
detectors by integrating several learning steps in a single algorithm, as well
as by proposing a number of improvements that accelerate detection
Backtracking Spatial Pyramid Pooling (SPP)-based Image Classifier for Weakly Supervised Top-down Salient Object Detection
Top-down saliency models produce a probability map that peaks at target
locations specified by a task/goal such as object detection. They are usually
trained in a fully supervised setting involving pixel-level annotations of
objects. We propose a weakly supervised top-down saliency framework using only
binary labels that indicate the presence/absence of an object in an image.
First, the probabilistic contribution of each image region to the confidence of
a CNN-based image classifier is computed through a backtracking strategy to
produce top-down saliency. From a set of saliency maps of an image produced by
fast bottom-up saliency approaches, we select the best saliency map suitable
for the top-down task. The selected bottom-up saliency map is combined with the
top-down saliency map. Features having high combined saliency are used to train
a linear SVM classifier to estimate feature saliency. This is integrated with
combined saliency and further refined through a multi-scale
superpixel-averaging of saliency map. We evaluate the performance of the
proposed weakly supervised topdown saliency and achieve comparable performance
with fully supervised approaches. Experiments are carried out on seven
challenging datasets and quantitative results are compared with 40 closely
related approaches across 4 different applications.Comment: 14 pages, 7 figure
Reduced Memory Region Based Deep Convolutional Neural Network Detection
Accurate pedestrian detection has a primary role in automotive safety: for
example, by issuing warnings to the driver or acting actively on car's brakes,
it helps decreasing the probability of injuries and human fatalities. In order
to achieve very high accuracy, recent pedestrian detectors have been based on
Convolutional Neural Networks (CNN). Unfortunately, such approaches require
vast amounts of computational power and memory, preventing efficient
implementations on embedded systems. This work proposes a CNN-based detector,
adapting a general-purpose convolutional network to the task at hand. By
thoroughly analyzing and optimizing each step of the detection pipeline, we
develop an architecture that outperforms methods based on traditional image
features and achieves an accuracy close to the state-of-the-art while having
low computational complexity. Furthermore, the model is compressed in order to
fit the tight constrains of low power devices with a limited amount of embedded
memory available. This paper makes two main contributions: (1) it proves that a
region based deep neural network can be finely tuned to achieve adequate
accuracy for pedestrian detection (2) it achieves a very low memory usage
without reducing detection accuracy on the Caltech Pedestrian dataset.Comment: IEEE 2016 ICCE-Berli
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