Attributes of Embodied Leadership: A beginning in the next chapter of leadership development

Research and guidance on leadership behaviour has been documented throughout history, from the epics to more recent leadership theories, evolved over the last century. Why then, when there is so much research and advice available are leaders still making so many errors? A review of literature in leadership studies reveals that recommendations have often been descriptive, assumptive and prescriptive without considering various differences in individuals. Additionally, leadership development often utilises methodologies in which individuals are trained to ‘act’ as leaders rather than fully embody leadership behaviour. This paper explores the generic attributes that describe embodied leadership behaviour. Semistructured interviews were performed on a panel of individuals from different backgrounds and analysed using a grounded theory approach. Along with the interviews, the works of Scharmer (2008) and behavioural traits identified in leadership by Derue, Nahrgang, Wellman and Humphrey (2011) were also taken into consideration. A final consensus was reached using a set of ten attributes that potentially contribute to embodied leadership behaviour; being non-judgemental, embracing uncertainty, active listening, congruence (morals and ethics), intuition, reflective practice, sense of meaning/purpose, holistic decision making, authentic presence and intention

Class-Weighted Convolutional Features for Visual Instance Search

Image retrieval in realistic scenarios targets large dynamic datasets of unlabeled images. In these cases, training or fine-tuning a model every time new images are added to the database is neither efficient nor scalable. Convolutional neural networks trained for image classification over large datasets have been proven effective feature extractors for image retrieval. The most successful approaches are based on encoding the activations of convolutional layers, as they convey the image spatial information. In this paper, we go beyond this spatial information and propose a local-aware encoding of convolutional features based on semantic information predicted in the target image. To this end, we obtain the most discriminative regions of an image using Class Activation Maps (CAMs). CAMs are based on the knowledge contained in the network and therefore, our approach, has the additional advantage of not requiring external information. In addition, we use CAMs to generate object proposals during an unsupervised re-ranking stage after a first fast search. Our experiments on two public available datasets for instance retrieval, Oxford5k and Paris6k, demonstrate the competitiveness of our approach outperforming the current state-of-the-art when using off-the-shelf models trained on ImageNet. The source code and model used in this paper are publicly available at http://imatge-upc.github.io/retrieval-2017-cam/.Comment: To appear in the British Machine Vision Conference (BMVC), September 201