GoodSAM: Bridging Domain and Capacity Gaps via Segment Anything Model
  for Distortion-aware Panoramic Semantic Segmentation
Weiming Zhang Yexin Liu Xu Zheng Lin Wang
This paper tackles a novel yet challenging problem: how to transfer knowledge
from the emerging Segment Anything Model (SAM) -- which reveals impressive
zero-shot instance segmentation capacity -- to learn a compact panoramic
semantic segmentation model, i.e., student, without requiring any labeled data.
This poses considerable challenges due to SAM's inability to provide semantic
labels and the large capacity gap between SAM and the student. To this end, we
propose a novel framework, called GoodSAM, that introduces a teacher assistant
(TA) to provide semantic information, integrated with SAM to generate ensemble
logits to achieve knowledge transfer. Specifically, we propose a
Distortion-Aware Rectification (DAR) module that first addresses the distortion
problem of panoramic images by imposing prediction-level consistency and
boundary enhancement. This subtly enhances TA's prediction capacity on
panoramic images. DAR then incorporates a cross-task complementary fusion block
to adaptively merge the predictions of SAM and TA to obtain more reliable
ensemble logits. Moreover, we introduce a Multi-level Knowledge Adaptation
(MKA) module to efficiently transfer the multi-level feature knowledge from TA
and ensemble logits to learn a compact student model. Extensive experiments on
two benchmarks show that our GoodSAM achieves a remarkable +3.75\% mIoU
improvement over the state-of-the-art (SOTA) domain adaptation methods. Also,
our most lightweight model achieves comparable performance to the SOTA methods
with only 3.7M parameters.