Social Attention: Modeling Attention in Human Crowds

Robots that navigate through human crowds need to be able to plan safe, efficient, and human predictable trajectories. This is a particularly challenging problem as it requires the robot to predict future human trajectories within a crowd where everyone implicitly cooperates with each other to avoid collisions. Previous approaches to human trajectory prediction have modeled the interactions between humans as a function of proximity. However, that is not necessarily true as some people in our immediate vicinity moving in the same direction might not be as important as other people that are further away, but that might collide with us in the future. In this work, we propose Social Attention, a novel trajectory prediction model that captures the relative importance of each person when navigating in the crowd, irrespective of their proximity. We demonstrate the performance of our method against a state-of-the-art approach on two publicly available crowd datasets and analyze the trained attention model to gain a better understanding of which surrounding agents humans attend to, when navigating in a crowd

Aesthetic attention

The aim of this paper is to give a new account of the way we exercise our attention in some paradigmatic cases of aesthetic experience. I treat aesthetic experience as a specific kind of experience and like in the case of other kinds of experiences, attention plays an important role in determining its phenomenal character. I argue that an important feature of at least some of our aesthetic experiences is that we exercise our attention in a specific, distributed, manner: our attention is focused on one perceptual object, but it is distributed among the various properties of this object. I argue that this way of exercising one’s attention is very different from the way we attend most of the time and it fits very well with some important features of paradigmatic examples of aesthetic experience

Attention, Please! Adversarial Defense via Attention Rectification and Preservation

This study provides a new understanding of the adversarial attack problem by examining the correlation between adversarial attack and visual attention change. In particular, we observed that: (1) images with incomplete attention regions are more vulnerable to adversarial attacks; and (2) successful adversarial attacks lead to deviated and scattered attention map. Accordingly, an attention-based adversarial defense framework is designed to simultaneously rectify the attention map for prediction and preserve the attention area between adversarial and original images. The problem of adding iteratively attacked samples is also discussed in the context of visual attention change. We hope the attention-related data analysis and defense solution in this study will shed some light on the mechanism behind the adversarial attack and also facilitate future adversarial defense/attack model design

Attention, Not Self

Jonardon Ganeri presents a radically reoriented account of mind, to which attention is the key. It is attention, not self, that explains the experiential and normative situatedness of humans in the world. Ganeri draws together three disciplines: analytic philosophy and phenomenology, cognitive science and psychology, and Buddhist thought