Jiwon Jason Choi 최지원

We present Intentable, a mixed-initiative caption authoring system that allows the author to steer an automatic caption generation process to reflect their intents on captions. We first derive grammar for specifying the intents, i.e., a caption recipe, and build a neural network that generates caption sentences given a recipe. Our quantitative evaluation revealed that our intent-based generation system not only allows the author to engage in the generation process but also produces more fluent captions than the previous end-to-end approaches without user intervention. Finally, we demonstrate the versatility of our system such as context adaptation, unit conversion, and sentence reordering.

In this paper, we present VANAS, a system that analyzes and visualizes the results of the Neural Architecture Search (NAS) algorithm. First, We devised several algorithms to quantify the significance and contribution of edges, and to recommend neural architecture to users, in order to efficiently convey the vast search results. We created an overview panel to check the overall characteristics of the search space, as well as a user interface to design and analyze the neural network, based on our algorithms. We demonstrated that by analyzing NAS-Bench-101 using VANAS, users can check the importance and significance of nodes and edges that compose neural networks, and that the accuracy can be increased by adding edges recommended by VANAS.