Current supervised visual detectors, though impressive within their training
distribution, often fail to segment out-of-distribution scenes into their
constituent entities. Recent test-time adaptation methods use auxiliary
self-supervised losses to adapt the network parameters to each test example
independently and have shown promising results towards generalization outside
the training distribution for the task of image classification. In our work, we
find evidence that these losses can be insufficient for instance segmentation
tasks, without also considering architectural inductive biases. For image
segmentation, recent slot-centric generative models break such dependence on
supervision by attempting to segment scenes into entities in a self-supervised
manner by reconstructing pixels. Drawing upon these two lines of work, we
propose Slot-TTA, a semi-supervised instance segmentation model equipped with a
slot-centric inductive bias, that is adapted per scene at test time through
gradient descent on reconstruction or novel view synthesis objectives. We show
that test-time adaptation in Slot-TTA greatly improves instance segmentation in
out-of-distribution scenes. We evaluate Slot-TTA in several 3D and 2D scene
instance segmentation benchmarks and show substantial out-of-distribution
performance improvements against state-of-the-art supervised feed-forward
detectors and self-supervised test-time adaptation methods.

本研究提出一种基于自我监督的 Slot-TTA 模型，在测试时通过梯度下降对重构或新颖视角综合目标进行场景自适应调整，大幅提高了场景内实例分割的准确性，尤其在非训练数据中取得了显著的性能提升。