This paper introduces a method for efficiently learning video-level object-centric representations by bootstrapping off a pre-trained image backbone, which we term Interpreter. It presents a novel hierarchical slot attention architecture with local learning and an optimal transport objective that yields fully unsupervised video segmentation. We first learn to compress images into image-level object-centric representations. Interpreter then learns to compress and reconstruct the object-centric representations for each frame across a video, allowing us to circumvent the costly process of reconstructing full frame feature maps. Unlike prior work, this allows us to scale to significantly longer videos without resorting to chunking videos into segments and matching between them. To deal with the unordered nature of object-centric representations, we employ Sinkhorn divergence, a relaxed optimal transport objective, to compute the distance between unordered sets of representations. We evaluate the resulting segmentation maps on video instance segmentation in both realistic and synthetic settings, using YTVIS-19 and MOVi-E, respectively. Interpreter achieves state-of-the-art results on the realistic YTVIS-19 dataset and presents a promising approach of scaling object-centric representation learning to longer videos (Code to be made publicly available at a later date).