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.

本文提出了一种名为 GoodSAM 的新型框架，通过引入教师助理 (TA) 并融合 SAM 的集成 logits 来解决从 Segment Anything Model (SAM) 到学生模型的知识转移问题，同时提出了 Distortion-Aware Rectification (DAR) 模块和 Multi-level Knowledge Adaptation (MKA) 模块来处理全景图像的畸变问题并进行多级特征知识的有效传递，实验证明 GoodSAM 在两个基准测试上取得了较先进方法 + 3.75% 的平均交并比 (mIoU) 提升，并且我们最轻量级的模型仅有 3.7M 个参数却达到了与先进方法相媲美的性能。

GoodSAM: 透过分段模型建立领域和能力差距，实现适应扭曲感知的全景语义分割

GoodSAM: Bridging Domain and Capacity Gaps via Segment Anything Model  for Distortion-aware Panoramic Semantic Segmentation

Model Soups, extending Stochastic Weights Averaging (SWA), combine models
fine-tuned with different hyperparameters. Yet, their adoption is hindered by
computational challenges due to subset selection issues. In this paper, we
propose to speed up model soups by approximating soups performance using
averaged ensemble logits performances. Theoretical insights validate the
congruence between ensemble logits and weight averaging soups across any mixing
ratios. Our Resource ADjusted soups craftINg (RADIN) procedure stands out by
allowing flexible evaluation budgets, enabling users to adjust his budget of
exploration adapted to his resources while increasing performance at lower
budget compared to previous greedy approach (up to 4% on ImageNet).

通过使用平均集成的日志绩效近似模型汤的性能，我们提出了一种加速模型汤的方法。理论上证实了集成日志与权重平均模型汤在任何混合比例下的一致性。我们的资源调整模型汤方法在允许灵活的评估预算的同时，相比以前的贪婪方法能够在更低的预算下提高性能（ImageNet 上高达 4%）。