Agenda

In this session, we will explore the latest advancements in computer vision that enable data-centric image classification model development. We will showcase how visual prompts and fast parameter-efficient models built on top of foundation models provide immediate feedback to rapidly iterate on data quality and model performance resulting in significant time-savings and performance improvements. Moreover, we will delve into the importance of adapting model representations via large-scale fine-tuning on weakly labeled data to address the limitations of fast but small models trained on fixed features. Finally, we will discuss the necessary scaling and model adaptations needed to transition from image-level classification to object-level detection and segmentation. Overall, this talk aims to provide insights into how computer vision data and models can be effectively improved in tandem and adjusted for downstream applications.