600 research outputs found
Learning Complexity-Aware Cascades for Deep Pedestrian Detection
The design of complexity-aware cascaded detectors, combining features of very
different complexities, is considered. A new cascade design procedure is
introduced, by formulating cascade learning as the Lagrangian optimization of a
risk that accounts for both accuracy and complexity. A boosting algorithm,
denoted as complexity aware cascade training (CompACT), is then derived to
solve this optimization. CompACT cascades are shown to seek an optimal
trade-off between accuracy and complexity by pushing features of higher
complexity to the later cascade stages, where only a few difficult candidate
patches remain to be classified. This enables the use of features of vastly
different complexities in a single detector. In result, the feature pool can be
expanded to features previously impractical for cascade design, such as the
responses of a deep convolutional neural network (CNN). This is demonstrated
through the design of a pedestrian detector with a pool of features whose
complexities span orders of magnitude. The resulting cascade generalizes the
combination of a CNN with an object proposal mechanism: rather than a
pre-processing stage, CompACT cascades seamlessly integrate CNNs in their
stages. This enables state of the art performance on the Caltech and KITTI
datasets, at fairly fast speeds
DEEP FULLY RESIDUAL CONVOLUTIONAL NEURAL NETWORK FOR SEMANTIC IMAGE SEGMENTATION
Department of Computer Science and EngineeringThe goal of semantic image segmentation is to partition the pixels of an image into semantically meaningful parts and classifying those parts according to a predefined label set. Although object recognition
models achieved remarkable performance recently and they even surpass human???s ability to recognize
objects, but semantic segmentation models are still behind. One of the reason that makes semantic
segmentation relatively a hard problem is the image understanding at pixel level by considering global
context as oppose to object recognition. One other challenge is transferring the knowledge of an object
recognition model for the task of semantic segmentation. In this thesis, we are delineating some of the
main challenges we faced approaching semantic image segmentation with machine learning algorithms.
Our main focus was how we can use deep learning algorithms for this task since they require the
least amount of feature engineering and also it was shown that such models can be applied to large scale
datasets and exhibit remarkable performance. More precisely, we worked on a variation of convolutional
neural networks (CNN) suitable for the semantic segmentation task. We proposed a model called deep
fully residual convolutional networks (DFRCN) to tackle this problem. Utilizing residual learning makes
training of deep models feasible which ultimately leads to having a rich powerful visual representation.
Our model also benefits from skip-connections which ease the propagation of information from the
encoder module to the decoder module. This would enable our model to have less parameters in the
decoder module while it also achieves better performance. We also benchmarked the effective variation
of the proposed model on a semantic segmentation benchmark.
We first make a thorough review of current high-performance models and the problems one might
face when trying to replicate such models which mainly arose from the lack of sufficient provided
information. Then, we describe our own novel method which we called deep fully residual convolutional
network (DFRCN). We showed that our method exhibits state of the art performance on a challenging
benchmark for aerial image segmentation.clos
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