  Obtaining the human-like perception ability of abstracting visual concepts
from concrete pixels has always been a fundamental and important target in
machine learning research fields such as disentangled representation learning
and scene decomposition. Towards this goal, we propose an unsupervised
transformer-based Visual Concepts Tokenization framework, dubbed VCT, to
perceive an image into a set of disentangled visual concept tokens, with each
concept token responding to one type of independent visual concept.
Particularly, to obtain these concept tokens, we only use cross-attention to
extract visual information from the image tokens layer by layer without
self-attention between concept tokens, preventing information leakage across
concept tokens. We further propose a Concept Disentangling Loss to facilitate
that different concept tokens represent independent visual concepts. The
cross-attention and disentangling loss play the role of induction and mutual
exclusion for the concept tokens, respectively. Extensive experiments on
several popular datasets verify the effectiveness of VCT on the tasks of
disentangled representation learning and scene decomposition. VCT achieves the
state of the art results by a large margin.
