Humean Effective Strategies

In a now-classic paper, Nancy Cartwright argued that the Humean conception of causation as mere regular co-occurrence is too weak to make sense of our everyday and scientific practices. Specifically she claimed that in order to understand our reasoning about, and uses of, effective strategies, we need a metaphysically stronger notion of causation and causal laws than Humeanism allows. Cartwright’s arguments were formulated in the framework of probabilistic causation, and it is precisely in the domain of (objective) probabilities that I am interested in defending a form of Humeanism. In this paper I will unpack some examples of effective strategies and discuss how well they fit the framework of causal laws and criteria such as CC from Cartwright’s and others’ works on probabilistic causality. As part of this discussion, I will also consider the concept or concepts of objective probability presupposed in these works. I will argue that Cartwright’s notion of a nomological machine, or a mechanism as defined by Stuart Glennan, is better suited for making sense of effective strategies, and therefore that a metaphysically primitive notion of causal law (or singular causation, or capacity, as Cartwright argues in (1989)) is not – here, at least – needed. These conclusions, as well as the concept of objective probabilities I defend, are largely in harmony with claims Cartwright defends in The Dappled World. My discussion aims, thus, to bring out into the open how far Cartwright’s current views are from a radically anti-Humean, causal-fundamentalist picture

Active Object Localization in Visual Situations

We describe a method for performing active localization of objects in instances of visual situations. A visual situation is an abstract concept---e.g., "a boxing match", "a birthday party", "walking the dog", "waiting for a bus"---whose image instantiations are linked more by their common spatial and semantic structure than by low-level visual similarity. Our system combines given and learned knowledge of the structure of a particular situation, and adapts that knowledge to a new situation instance as it actively searches for objects. More specifically, the system learns a set of probability distributions describing spatial and other relationships among relevant objects. The system uses those distributions to iteratively sample object proposals on a test image, but also continually uses information from those object proposals to adaptively modify the distributions based on what the system has detected. We test our approach's ability to efficiently localize objects, using a situation-specific image dataset created by our group. We compare the results with several baselines and variations on our method, and demonstrate the strong benefit of using situation knowledge and active context-driven localization. Finally, we contrast our method with several other approaches that use context as well as active search for object localization in images.Comment: 14 page