Modern computer vision systems achieve strong performance, but often depend on extensive manual annotations and substantial computational resources. This PhD seminar explores how the structure of visual data can help overcome these limitations, focusing on learning segmentation with limited supervision and enabling efficient visual processing.
I will begin with semi-supervised semantic segmentation, showing how spatial relationships between neighboring predictions can improve pseudo-label reliability and make better use of unlabeled images. I will then turn to instance segmentation for robotic grasping; beyond annotation scarcity, I will examine annotation quality, introducing controlled benchmarks that reveal how spatial errors and automatically generated masks affect segmentation performance.
The second part extends this efficiency perspective to vision–language models (VLMs). I will present approaches that predict sufficient input resolution, retrieve high-resolution regions when additional evidence is needed, and progressively encode visual detail using wavelet representations and computation reuse. These approaches adapt visual processing to the requirements of each task, offering control over the trade-off between accuracy and computational cost.
Together, these studies investigate how visual structure and task-dependent information can support effective learning and inference with fewer resources.