A Deep Learning Approach to Landmark Detection in Facial Images

In this paper an alternative approach to landmark detection using cascaded convolutional neural networks is proposed. The cascade consists of three different levels, each with a number of convolutional neural networks. After each layer of the cascade, predictions converge. This results in accurate predictions of landmark locations in facial images. The main advantage over other methods proposed in literature is the integration of face detection and landmark detection in one system. Also, the method is both able to implicitly encode local constraints and shape constraints over the entire image, thus giving it an advantage over regular non-deep learning detection methods. As such, the cascaded neural network substantially outperforms STASM, a state-of-the-art method shape model approach. However, the model does not hold well for data that is not similar to the images it has been trained on

MorphPool: Efficient Non-linear Pooling & Unpooling in CNNs

Pooling is essentially an operation from the field of Mathematical Morphology, with max pooling as a limited special case. The more general setting of MorphPooling greatly extends the tool set for building neural networks. In addition to pooling operations, encoder-decoder networks used for pixel-level predictions also require unpooling. It is common to combine unpooling with convolution or deconvolution for up-sampling. However, using its morphological properties, unpooling can be generalised and improved. Extensive experimentation on two tasks and three large-scale datasets shows that morphological pooling and unpooling lead to improved predictive performance at much reduced parameter counts.Comment: Accepted paper at the British Machine Vision Conference (BMVC) 202

Multi-Loss Weighting with Coefficient of Variations

Many interesting tasks in machine learning and computer vision are learned by optimising an objective function defined as a weighted linear combination of multiple losses. The final performance is sensitive to choosing the correct (relative) weights for these losses. Finding a good set of weights is often done by adopting them into the set of hyper-parameters, which are set using an extensive grid search. This is computationally expensive. In this paper, we propose a weighting scheme based on the coefficient of variations and set the weights based on properties observed while training the model. The proposed method incorporates a measure of uncertainty to balance the losses, and as a result the loss weights evolve during training without requiring another (learning based) optimisation. In contrast to many loss weighting methods in literature, we focus on single-task multi-loss problems, such as monocular depth estimation and semantic segmentation, and show that multi-task approaches for loss weighting do not work on those single-tasks. The validity of the approach is shown empirically for depth estimation and semantic segmentation on multiple datasets.Comment: Paper was accepted at the IEEE Winter Conference on Applications of Computer Vision 2021 (WACV2021

Benefits of Social Learning in Physical Robots

Robot-to-robot learning, a specific case of social learning in robotics, enables the ability to transfer robot controllers directly from one robot to another. Previous studies showed that the exchange of controller information can increase learning speed and performance. However, most of these studies have been performed in simulation, where robots are identical. Therefore, the results do not necessarily transfer to a real environment, where each robot is unique per definition due to the random differences in hardware. In this paper, we investigate the effect of exchanging controller information, on top of individual learning, in a group of Thymio II robots for two tasks: obstacle avoidance and foraging. The controllers of the robots are neural networks that evolve using a modified version of the state-of-the-art NEAT algorithm, called cNEAT, which allows the conversion of innovations numbers from other robots. This paper shows that robot-to-robot learning seems to at least parallelise the search, reducing wall clock time. Additionally, controllers are less complex, resulting in a smaller search space

The value of open-source clinical science in pandemic response: lessons from ISARIC

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