In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

Classes in natural images tend to follow long tail distributions. This is problematic when there are insufficient training examples for rare classes. This effect is emphasized in compound classes, involving the conjunction of several concepts, such as those appearing in action-recognition datasets. In this paper, we propose to address this issue by learning how to utilize common visual concepts which are readily available. We detect the presence of prominent concepts in images and use them to infer the target labels instead of using visual features directly, combining tools from vision and natural-language processing. We validate our method on the recently introduced HICO dataset reaching a mAP of 31.54% and on the Stanford40 Actions dataset, where the proposed method outperforms current state-of-the art and, combined with direct visual features, obtains an accuracy 83.12%. Moreover, the method provides for each class a semantically meaningful list of keywords and relevant image regions relating it to its constituent concepts.
Action Classification via Concepts and Attributes

Potential games and decentralised partially observable MDPs (Dec–POMDPs) are two commonly used models of multi–agent interaction, for static optimisation and sequential decision– making settings, respectively. In this paper we introduce filtered fictitious play for solving repeated potential games in which each player’s observations of others’ actions are perturbed by random noise, and use this algorithm to construct an online learning method for solving Dec–POMDPs. Specifically, we prove that noise in observations prevents standard fictitious play from converging to Nash equilibrium in potential games, which also makes fictitious play impractical for solving Dec–POMDPs. To combat this, we derive filtered fictitious play, and provide conditions under which it converges to a Nash equilibrium in potential games with noisy observations. We then use filtered fictitious play to construct a solver for Dec–POMDPs, and demonstrate our new algorithm’s performance in a box pushing problem. Our results show that we consistently outperform the state–of–the–art Dec– POMDP solver by an average of 100% across the range of noise in the observation function.
Filtered Fictitious Play for Perturbed Observation Potential Games and Decentralised POMDPs

<lb>We study a general version of the adversarial online learning problem. We are given a decision<lb>set X in a reflexive Banach space X and a sequence of reward vectors in the dual space of X . At<lb>each iteration, we choose an action from X , based on the observed sequence of previous rewards.<lb>Our goal is to minimize regret, defined as the gap between the realized reward and the reward<lb>of the best fixed action in hindsight. Using results from infinite dimensional convex analysis, we<lb>generalize the method of Dual Averaging (or Follow the Regularized Leader) to our setting and<lb>obtain general upper bounds on the worst-case regret that subsume a wide range of results from the<lb>literature. Under the assumption of uniformly continuous rewards, we obtain explicit anytime regret<lb>bounds in a setting where the decision set is the set of probability distributions on a compact metric<lb>space S whose Radon-Nikodym derivatives are elements of L(S) for some p > 1. Importantly,<lb>we make no convexity assumptions on either the set S or the reward functions. We also prove<lb>a general lower bound on the worst-case regret for any online algorithm. We then apply these<lb>results to the problem of learning in repeated continuous two-player zero-sum games, in which<lb>players’ strategy sets are compact metric spaces. In doing so, we first prove that if both players<lb>play a Hannan-consistent strategy, then with probability 1 the empirical distributions of play weakly<lb>converge to the set of Nash equilibria of the game. We then show that, under mild assumptions,<lb>Dual Averaging on the (infinite-dimensional) space of probability distributions indeed achieves<lb>Hannan-consistency. Finally, we illustrate our results through numerical examples.<lb>

Minimizing Regret on Reflexive Banach Spaces and Learning Nash Equilibria in Continuous Zero-Sum Games