No Adversaries to Zero-Shot Learning: Distilling an Ensemble of Gaussian Feature Generators.

In zero-shot learning (ZSL), the task of recognizing unseen categories when no data for training is available, state-of-the-art methods generate visual features from semantic auxiliary information (e.g., attributes). In this work, we propose a valid alternative (simpler, yet better scoring) to fulfill the very same task. We observe that, if first- and second-order statistics of the classes to be recognized were known, sampling from Gaussian distributions would synthesize visual features that are almost identical to the real ones as per classification purposes. We propose a novel mathematical framework to estimate first- and second-order statistics, even for unseen classes: our framework builds upon prior compatibility functions for ZSL and does not require additional training. Endowed with such statistics, we take advantage of a pool of class-specific Gaussian distributions to solve the feature generation stage through sampling. We exploit an ensemble mechanism to aggregate a pool of softmax classifiers, each trained in a one-seen-class-out fashion to better balance the performance over seen and unseen classes. Neural distillation is finally applied to fuse the ensemble into a single architecture which can perform inference through one forward pass only. Our method, termed Distilled Ensemble of Gaussian Generators, scores favorably with respect to state-of-the-art works.

Understanding action concepts from videos and brain activity through subjects' consensus.

In this paper, we investigate brain activity associated with complex visual tasks, showing that electroencephalography (EEG) data can help computer vision in reliably recognizing actions from video footage that is used to stimulate human observers. Notably, we consider not only typical "explicit" video action benchmarks, but also more complex data sequences in which action concepts are only referred to, implicitly. To this end, we consider a challenging action recognition benchmark dataset-Moments in Time-whose video sequences do not explicitly visualize actions, but only implicitly refer to them (e.g., fireworks in the sky as an extreme example of "flying"). We employ such videos as stimuli and involve a large sample of subjects to collect a high-definition, multi-modal EEG and video data, designed for understanding action concepts. We discover an agreement among brain activities of different subjects stimulated by the same video footage. We name it as subjects consensus, and we design a computational pipeline to transfer knowledge from EEG to video, sharply boosting the recognition performance.

The human brain reveals resting state activity patterns that are predictive of biases in attitudes toward robots.

The increasing presence of robots in society necessitates a deeper understanding into what attitudes people have toward robots. People may treat robots as mechanistic artifacts or may consider them to be intentional agents. This might result in explaining robots' behavior as stemming from operations of the mind (intentional interpretation) or as a result of mechanistic design (mechanistic interpretation). Here, we examined whether individual attitudes toward robots can be differentiated on the basis of default neural activity pattern during resting state, measured with electroencephalogram (EEG). Participants observed scenarios in which a humanoid robot was depicted performing various actions embedded in daily contexts. Before they were introduced to the task, we measured their resting state EEG activity. We found that resting state EEG beta activity differentiated people who were later inclined toward interpreting robot behaviors as either mechanistic or intentional. This pattern is similar to the pattern of activity in the default mode network, which was previously demonstrated to have a social role. In addition, gamma activity observed when participants were making decisions about a robot's behavior indicates a relationship between theory of mind and said attitudes. Thus, we provide evidence that individual biases toward treating robots as either intentional agents or mechanistic artifacts can be detected at the neural level, already in a resting state EEG signal.