Pipeline To Generate Training Data For Image Recognition

Image recognition programs require large sets of training data to produce accurate results. Human workers may categorize training sets that programs may use as training data to learn how to recognize objects. To increase the efficiency of the workers, it is proposed to break the categorization down into multiple steps in a pipeline. Different groups of workers will provide input at different stages of the pipeline, and the input from one group of workers will be passed to another group of workers. Breaking the categorization down into smaller tasks may increase the efficiency of the workers

Revisiting adapters with adversarial training

While adversarial training is generally used as a defense mechanism, recent works show that it can also act as a regularizer. By co-training a neural network on clean and adversarial inputs, it is possible to improve classification accuracy on the clean, non-adversarial inputs. We demonstrate that, contrary to previous findings, it is not necessary to separate batch statistics when co-training on clean and adversarial inputs, and that it is sufficient to use adapters with few domain-specific parameters for each type of input. We establish that using the classification token of a Vision Transformer (ViT) as an adapter is enough to match the classification performance of dual normalization layers, while using significantly less additional parameters. First, we improve upon the top-1 accuracy of a non-adversarially trained ViT-B16 model by +1.12% on ImageNet (reaching 83.76% top-1 accuracy). Second, and more importantly, we show that training with adapters enables model soups through linear combinations of the clean and adversarial tokens. These model soups, which we call adversarial model soups, allow us to trade-off between clean and robust accuracy without sacrificing efficiency. Finally, we show that we can easily adapt the resulting models in the face of distribution shifts. Our ViT-B16 obtains top-1 accuracies on ImageNet variants that are on average +4.00% better than those obtained with Masked Autoencoders

A Realistic Simulator for the Design and Evaluation of Intelligent Vehicles

The number of vehicles hitting the road each day is rapidly increasing, and several problems, such as traffic conges- tion or driver safety, can no longer be solved in the same fashion as before. Intelligent transportation systems could potentially solve part of these problems, but prototyping, designing and testing cooperative smart vehicles is a cumbersome task. This paper presents a realistic simulator, capable of operating both at microscopic and sub-microscopic level, where intelligent vehicles can be designed and analyzed with a pragmatic approach. A number of advances in robotics have already been transferred to vehicular technology, with a potential increase of this trend into the future. Here, we develop a plugin for a well-established robotics simulator (Webots), in order to reinforce at the virtual level this cross-fertilization between the two areas and create a baseline for realistic studies of future solutions in real intelligent vehicles

Two-Phase Online Calibration for Infrared-based Inter-Robot Positioning Modules

Multi-robot systems can solve complex tasks that require the coordination of the team-member positions with respect to each other.While the development of ad-hoc relative positioning platforms embedding cheap off-the-shelf components is a practical choice, it leads not only to differences between the platforms themselves, but also to a high sensitivity to external factors. In this paper, we present a novel lightweight online calibration method composed of two phases, capable of running on miniature robots with limited computational capabilities. Furthermore, by exploiting a Gaussian process regression in its second phase, the proposed calibration approach is able to capture deviations from an assumed underlying physical model. We compare the performance of our approach with the theoretical Cramer-Rao lower bound and test its efficiency on real robots equipped with range and bearing modules

Local Graph-based Distributed Control for Safe Highway Platooning

Using graph theory, this paper investigates how a group of vehicles, endowed with local positioning capabilities (range and bearing to other vehicles), can keep a predefined formation. We propose a longitudinal and lateral controller that stabilizes a system of several vehicles as well as a collision avoidance mechanism. The stability of our approach is supported by a mathematical analysis as well as realistic simulations

Graph based distributed control of non-holonomic vehicles endowed with local positioning information engaged in escorting missions

Using graph theory, this paper investigates how a group of robots, endowed with local positioning (range and bearing from other robots), can be engaged in a leader- following mission whilst keeping a predefined configuration. The possibility to locally change the behaviors of the follower team to accomodate both tasks is explored. In particular, a methodology to automatically adjust the parameters of the inter-robot interactions and a nonlinear PI controller are explained and implemented. Our approach is supported by a mathematical analysis as well as real robot experiments