Learning Aggregation Functions for Writer Retrieval
Status: taken Supervisors: Marco Peer, Florian Kleber Deep-learning-based methods for writer retrieval make use of sampling local characteristics of handwriting, for example using patches extracted at SIFT keypoint locations(see Figure 1), to learn discriminative features. To compute a global page descriptor of those local embeddings, state-of-the-art methods rely on fixed aggregation functions, e.g. sum/average pooling … Continue reading Learning Aggregation Functions for Writer Retrieval