6,330,819 research outputs found
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
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