HASSOD: Hierarchical Adaptive Self-Supervised Object Detection
Shengcao Cao Dhiraj Joshi Liang-Yan Gui Yu-Xiong Wang
The human visual perception system demonstrates exceptional capabilities in
learning without explicit supervision and understanding the part-to-whole
composition of objects. Drawing inspiration from these two abilities, we
propose Hierarchical Adaptive Self-Supervised Object Detection (HASSOD), a
novel approach that learns to detect objects and understand their compositions
without human supervision. HASSOD employs a hierarchical adaptive clustering
strategy to group regions into object masks based on self-supervised visual
representations, adaptively determining the number of objects per image.
Furthermore, HASSOD identifies the hierarchical levels of objects in terms of
composition, by analyzing coverage relations between masks and constructing
tree structures. This additional self-supervised learning task leads to
improved detection performance and enhanced interpretability. Lastly, we
abandon the inefficient multi-round self-training process utilized in prior
methods and instead adapt the Mean Teacher framework from semi-supervised
learning, which leads to a smoother and more efficient training process.
Through extensive experiments on prevalent image datasets, we demonstrate the
superiority of HASSOD over existing methods, thereby advancing the state of the
art in self-supervised object detection. Notably, we improve Mask AR from 20.2
to 22.5 on LVIS, and from 17.0 to 26.0 on SA-1B. Project page:
https://HASSOD-NeurIPS23.github.io.