The emergence of language between artificial agents is a recent focus of computational linguistics, as it offers a synthetic substrate for reasoning about human language evolution.From the perspective of cognitive science, sophisticated categorization in humans is thought to enable reasoning about novel observations, and thus compose old information to describe new phenomena.Unfortunately, the literature to date has not managed to isolate the effect of categorization power in artificial agents on their inter-communication ability, particularly on novel, unseen objects.In this work, we propose the use of disentangled representations from representation learning to quantify the categorization power of agents, enabling a differential analysis between combinations of heterogeneous systems, e.g., pairs of agents which learn to communicate despite mismatched concept realization.Through this approach, we observe that agent heterogeneity can cut signaling accuracy by up to 40%, despite encouraging compositionality in the artificial language.We conclude that the reasoning process of agents plays a key role in their communication, with unexpected benefits arising from their mixing, such as better language compositionality.

Entropy-based divergence measures have proven their effectiveness in many areas of computer vision and pattern recognition. However, the complexity of their implementation might be prohibitive in resource-limited applications, as they require estimates of probability densities which are expensive to compute directly for high-dimensional data. In this paper, we investigate the usage of a non-parametric distribution-free metric, known as the Henze-Penrose test statistic to obtain bounds for the $k$ -nearest neighbors ( $k$ -NN) classification accuracy. Simulation results demonstrate the effectiveness and the reliability of this metric in estimating the inter-class separability. In addition, the proposed bounds on the $k$ -NN classification are exploited for evaluating the efficacy of different pre-processing techniques as well as selecting the least number of features that would achieve the desired classification performance.

Analysis procedures for higher-dimensional data are generally computationally costly; thereby justifying the high research interest in the area. Entropy-based divergence measures have proven their effectiveness in many areas of computer vision and pattern recognition. However, the complexity of their implementation might be prohibitive in resource-limited applications, as they require estimates of probability densities which are very difficult to compute directly for high-dimensional data. In this paper, we investigate the usage of a non-parametric distribution-free metric, known as the Henze-Penrose test statistic, to estimate the divergence between different classes of vehicles. In this regard, we apply some common feature extraction techniques to further characterize the distributional separation relative to the original data. Moreover, we employ the Henze-Penrose metric to obtain bounds for the Nearest Neighbor (NN) classification accuracy. Simulation results demonstrate the effectiveness and the reliability of this metric in estimating the inter-class separability. In addition, the proposed bounds are exploited for selecting the least number of features that would retain sufficient discriminative information.

In applications where the use of video surveillance is necessary and/or beneficial, it is a common goal to identify the contents of the video automatically. Of particular interest in such applications is the ability to recognize locations in the environment, where events occur, and describe the events common to those locations. This is one of the goals of scene understanding. Scene understanding is traditionally addressed from one of two separate points-of-view: the description of the underlying environment or the action taking-place throughout the scene. Each of these facets is required to address the overarching goal but, is insufficient independently to address the problem entirely. These facets are, in fact, dependent and by considering both, a more complete description becomes available. In this paper, we describe a novel, data-driven scene understanding and classification technique that captures and utilizes information about both the environment and activity within a scene.