Semantic segmentation was seen as a challenging computer vision problem few years ago.
Due to recent advancements in deep learning, relatively accurate solutions are now …

Dynamic model pruning is a recent direction that allows for the inference of a different sub-network
for each input sample during deployment. However, current dynamic methods rely on …

Recent advances in pruning of neural networks have made it possible to remove a large
number of filters or weights without any perceptible drop in accuracy. The number of parameters …

Neural network quantization has achieved a high compression rate using a fixed low bit-width
representation of weights and activations while maintaining the accuracy of the high-…

Convolutional neural networks (CNNs) have emerged as the state-of-the-art in multiple vision
tasks including depth estimation. However, memory and computing power requirements …

… Static, the Dynamic and the Shallow by Sara Elkerdawy A thesis submitted in partial fulfil …
This thesis is an original work by Sara Elkerdawy. The research project, of which this thesis is a …

Vehicle fine-grained classification is a challenging research problem with little attention in
the field. In this paper, we propose a deep network architecture for vehicles fine-grained …

Recent advances in neural networks pruning have made it possible to remove a large number
of filters without any perceptible drop in accuracy. However, the gain in speed depends …

This paper presents a novel real-time scale adaptive visual tracking framework and its use
in smart traffic monitoring where the framework robustly detects and tracks vehicles from a …

Tracking by detection techniques have recently been gaining increased attention in visual
object tracking due to their promising results in applications such as robotics, surveillance, …