A Convolutional Neural Network (CNN) relies on intricate pixel correlations to predict objects within images, demonstrating improved performance when identifying objects alongside commonly co-occurring objects. For instance, a helicopter can be more accurately predicted when it appears with an aircraft carrier compared to when it stands alone. Influence on detecting an object due to the presence or absence of another object can be regarded as a form of contextual bias. This phenomenon poses a significant challenge in object detection. Addressing this bias is crucial. Our study explores correlation-aware prediction techniques to disentangle and leverage connections between objects. We propose CART-OD, a novel object detection approach that enhances model performance by adjusting the confidence scores of correlated objects during inference. By strategically intervening during prediction, CART-OD targets instances where commonly co-occurring objects appear separately, effectively mitigating the influence of object correlation. The adjustment in real-time makes the approach computationally efficient and scalable to handle large-scale datasets. Results indicate that CART-OD significantly enhances precision, recall, and mean average precision (mAP) in detecting objects that appear without their most correlated counterparts, underscoring the potential of correlation-aware strategies in object detection tasks.

Normal form games are one of the most familiar representations for modeling interactions among multiple agent. However, modeling many realistic interactions between agents results in games that are extremely large. In these cases computing standard solutions like Nash equilibrium may be intractable. To overcome this issue the idea of abstraction has been investigated, most prominently in research on computer Poker. Solving a game using abstraction requires using some method to simplify the game before it is analyzed. We study a new variation for solving normal form games using abstraction that is based on finding and solving suitable sub games. We compare this method with several variations of a common type of abstraction based on clustering similar strategies.