Learn More
We propose a simple and straightforward way of creating powerful image representations via cross-dimensional weighting and aggregation of deep convolutional neural network layer outputs. We first present a generalized framework that encompasses a broad family of approaches and includes cross-dimensional pooling and weighting steps. We then propose specific(More)
We propose a novel hashing-based matching scheme, called Locally Optimized Hashing (LOH), based on a state-of-the-art quantization algorithm that can be used for efficient, large-scale search, recommendation, clustering, and deduplication. We show that matching with LOH only requires set intersections and summations to compute and so is easily implemented(More)
We investigate the patterns and evolution of cultural ideas and symbols in media using network analysis techniques. We leverage the TV Tropes wiki of media works, cross-referenced with the widely-recognized character/situation types (tropes) that these works contain, to construct a bipartite graph representation of 4,616 films and their associated tropes.(More)
  • 1