Benchmarking Unsupervised Object Representations for Video Sequences

Perceiving the world in terms of objects and tracking them through time is a crucial prerequisite for reasoning and scene understanding. Recently, several methods have been proposed for unsupervised learning of object-centric representations. However, since these models have been evaluated with respect to different downstream tasks, it remains unclear how they compare in terms of basic perceptual abilities such as detection, figure-ground segmentation and tracking of individual objects. To close this gap, we design a benchmark with three datasets of varying complexity and seven additional test sets which feature challenging tracking scenarios relevant for natural videos. Using this benchmark, we compare the perceptual abilities of four unsupervised object-centric learning approaches: ViMON, a video-extension of MONet, based on a recurrent spatial attention mechanism, OP3, which exploits clustering via spatial mixture models, as well as TBA and SCALOR, which use an explicit factorization via spatial transformers. Our results suggest that architectures with unconstrained latent representations and full-image object masks such as ViMON and OP3 are able to learn more powerful representations in terms of object detection, segmentation and tracking than the explicitly parameterized spatial transformer based architecture of TBA and SCALOR. We also observe that none of the methods are able to gracefully handle the most challenging tracking scenarios despite their synthetic nature, suggesting that our benchmark may provide fruitful guidance towards learning more robust object-centric video representations.