5 accepted papers
Multi-task learning (MTL) aims to enable a single model to solve multiple tasks efficiently; however, current parameter-efficient fine-tuning (PEFT) methods remain largely limited to single-task adaptation. We introduce Free Sinewich, a parameter-efficient multi-task learning framework that enables
Player tracking is a fundamental task in sports video understanding. Many technical challenges should be addressed due to irregular movement, occlusion between players, and complex background. In this work, we present a framework that utilizes synchronized videos captured from multiple view-points.…
In one-shot NAS, sub-networks need to be searched from the supernet to meet different hardware constraints. However, the search cost is high and N times of searches are needed for N different constraints. In this work, we propose a novel search strategy called architecture generator to search sub-ne…
A latent style model describing manga styles based on the proposed manga-specific features is constructed to facilitate novel style-based applications. Two manga-specific features, i.e., screentone features showing texture and shade, and panel features showing panel arrangement, are firstly proposed…
An automatic news story clustering system is presented to facilitate efficient news browsing and summarization. We describe news content by considering both what objects appear and how these objects move in news stories. With Fisher embedding, we respectively encode local features, semantics feature…