Cross-domain Variational Capsules for Information Extraction
This work addresses the challenge of fine-grained characteristic recognition in images across multiple domains, though it appears incremental by combining existing methods.
The paper tackled the problem of cross-domain information extraction by proposing a combination of Variational Autoencoders and Capsule Networks to identify prominent characteristics in data and auto-generate insights for unseen domains, resulting in the creation of the Multi-domain Image Characteristics Dataset with thousands of images across three domains as a new benchmark.
In this paper, we present a characteristic extraction algorithm and the Multi-domain Image Characteristics Dataset of characteristic-tagged images to simulate the way a human brain classifies cross-domain information and generates insight. The intent was to identify prominent characteristics in data and use this identification mechanism to auto-generate insight from data in other unseen domains. An information extraction algorithm is proposed which is a combination of Variational Autoencoders (VAEs) and Capsule Networks. Capsule Networks are used to decompose images into their individual features and VAEs are used to explore variations on these decomposed features. Thus, making the model robust in recognizing characteristics from variations of the data. A noteworthy point is that the algorithm uses efficient hierarchical decoding of data which helps in richer output interpretation. Noticing a dearth in the number of datasets that contain visible characteristics in images belonging to various domains, the Multi-domain Image Characteristics Dataset was created and made publicly available. It consists of thousands of images across three domains. This dataset was created with the intent of introducing a new benchmark for fine-grained characteristic recognition tasks in the future.