14 DAILY CVPR Thursday knowledge and ensure exposure to diverse data. The availability of unlabeled data on a much larger scale than labeled data presents an opportunity to adopt a weakly or semi-supervised framework in future iterations of this work. A notable aspect of this research is its exclusive focus on point trajectories without relying on visual data. This focus aims to push the limits of what can be achieved with geometric data alone. The approach’s effectiveness was validated through extensive experiments, which showed stateof-the-art performance for trajectory-based motion segmentation on full sequences and competitive results on occluded sequences. Consequently, a challenge this work has not fully addressed is the high rate of occlusions common in realworld scenarios. “We have an algorithm for data completion, and for major corruptions, it’s converging, but it’s not working as fast and as nice,” Yaroslava reveals. “There are different ways to address that, and we are looking into using global context information.” Away from this paper, Yaroslava’s regular work covers similar ground but on a broader scale. “I’m looking into various techniques in geometry optimization and machine learning to solve 3D vision problems,” she tells us. “I’m looking into different ways to combine them meaningfully to take the best of all worlds!” To learn more about Yaroslava’s work, visit Poster Session 4 & Exhibit Hall (Arch 4A-E) from 17:15 to 18:45 [Poster 433]. “I’m looking into different ways to combine them meaningfully to take the best of all worlds!” UKRAINE CORNER Poster Presentation
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