The Influence of Grammatical Gender on Russian and Thai Speakers’ Cognition

The aim of this study is to investigate the influence of grammatical gender on Russian speakers’ cognition, compared with Thai speakers’ cognition by means of object categorization. The key materials in the experiment are black-and-white pictures represented by nouns that are selected based on gender and appearance similarity. The hypothesis is that Russian speakers group two pictures that belong to the same grammatical gender class together, while Thai speakers generally rely on the size or shape of objects in the pictures. The result of the experiment statistically showed that Russian speakers categorized things on the basis of grammatical gender, while Thai speakers categorized things represented by things grouped on the basis of size or shape. Additionally, the result implies that bilingualism is a very important variable in a study testing the Linguistic Relativity Hypothesis.

A Similarity Measure for Material Appearance

We present a model to measure the similarity in appearance between different materials, which correlates with human similarity judgments. We first create a database of 9,000 rendered images depicting objects with varying materials, shape and illumination. We then gather data on perceived similarity from crowdsourced experiments; our analysis of over 114,840 answers suggests that indeed a shared perception of appearance similarity exists. We feed this data to a deep learning architecture with a novel loss function, which learns a feature space for materials that correlates with such perceived appearance similarity. Our evaluation shows that our model outperforms existing metrics. Last, we demonstrate several applications enabled by our metric, including appearance-based search for material suggestions, database visualization, clustering and summarization, and gamut mapping.

Learn from experience: Probabilistic prediction of perception performance to avoid failure

Despite significant advances in machine learning and perception over the past few decades, perception algorithms can still be unreliable when deployed in challenging time-varying environments. When these systems are used for autonomous decision-making, such as in self-driving vehicles, the impact of their mistakes can be catastrophic. As such, it is important to characterize the performance of the system and predict when and where it may fail in order to take appropriate action. While similar in spirit to the idea of introspection, this work introduces a new paradigm for predicting the likely performance of a robot’s perception system based on past experience in the same workspace. In particular, we propose two models that probabilistically predict perception performance from observations gathered over time. While both approaches are place-specific, the second approach additionally considers appearance similarity when incorporating past observations. We evaluate our method in a classical decision-making scenario in which the robot must choose when and where to drive autonomously in 60km of driving data from an urban environment. Results demonstrate that both approaches lead to fewer false decisions (in terms of incorrectly offering or denying autonomy) for two different detector models, and show that leveraging visual appearance within a state-of-the-art navigation framework increases the accuracy of our performance predictions.