ClevrTex: A Texture-Rich Benchmark for Unsupervised Multi-Object Segmentation

Understand

There has been a recent surge in methods that aim to decompose and segment scenes into multiple objects in an unsupervised manner, i.e., unsupervised multi-object segmentation.

• Performing such a task is a long-standing goal of computer vision, offering to unlock object-level reasoning without requiring dense annotations to train segmentation models.
• Despite significant progress, current models are developed and trained on visually simple scenes depicting mono-colored objects on plain backgrounds.
• The natural world, however, is visually complex with confounding aspects such as diverse textures and complicated lighting effects.