Deploying deep learning-based applications in specialized domains like the
aircraft production industry typically suffers from the training data
availability problem. Only a few datasets represent non-everyday objects,
situations, and tasks. Recent advantages in research around Vision Foundation
Models (VFM) opened a new area of tasks and models with high generalization
capabilities in non-semantic and semantic predictions. As recently demonstrated
by the Segment Anything Project, exploiting VFM's zero-shot capabilities is a
promising direction in tackling the boundaries spanned by data, context, and
sensor variety. Although, investigating its application within specific domains
is subject to ongoing research. This paper contributes here by surveying
applications of the SAM in aircraft production-specific use cases. We include
manufacturing, intralogistics, as well as maintenance, repair, and overhaul
processes, also representing a variety of other neighboring industrial domains.
Besides presenting the various use cases, we further discuss the injection of
domain knowledge.

在航空制造专业领域中，部署基于深度学习的应用程序通常会遇到训练数据可用性的问题。最近在 Vision Foundation Models (VFM) 的研究优势方面，开辟了一个具有高泛化能力的非语义和语义预测的任务和模型的新领域。尽管如此，在特定领域内探索其应用仍在进行中。本文通过调研在航空生产中特定用例中 SAM 的应用来做出贡献，并包括制造、内部物流以及维护、修理和翻新过程，同时也涵盖了其他邻近的工业领域。除了展示各种用例外，我们进一步讨论了领域知识的注入。

工业领域细分 -- 航空制造业中的案例研究：内部物流、维修与修复

Industrial Segment Anything -- a Case Study in Aircraft Manufacturing,  Intralogistics, Maintenance, Repair, and Overhaul

Recent advancements in vision foundation models (VFMs) have opened up new
possibilities for versatile and efficient visual perception. In this work, we
introduce Seal, a novel framework that harnesses VFMs for segmenting diverse
automotive point cloud sequences. Seal exhibits three appealing properties: i)
Scalability: VFMs are directly distilled into point clouds, eliminating the
need for annotations in either 2D or 3D during pretraining. ii) Consistency:
Spatial and temporal relationships are enforced at both the camera-to-LiDAR and
point-to-segment stages, facilitating cross-modal representation learning. iii)
Generalizability: Seal enables knowledge transfer in an off-the-shelf manner to
downstream tasks involving diverse point clouds, including those from
real/synthetic, low/high-resolution, large/small-scale, and clean/corrupted
datasets. Extensive experiments conducted on eleven different point cloud
datasets showcase the effectiveness and superiority of Seal. Notably, Seal
achieves a remarkable 45.0% mIoU on nuScenes after linear probing, surpassing
random initialization by 36.9% mIoU and outperforming prior arts by 6.1% mIoU.
Moreover, Seal demonstrates significant performance gains over existing methods
across 20 different few-shot fine-tuning tasks on all eleven tested point cloud
datasets.

本文介绍一种名为 Seal 的新型框架，其利用视觉基础模型（VFMs）对不同类型的汽车点云序列进行分割，具有可扩展性、一致性和普适性，并在 11 种点云数据集上进行了广泛实验，取得了显著的性能提升。

利用视觉基础模型分离任意点云序列

Segment Any Point Cloud Sequences by Distilling Vision Foundation Models

Vision foundation models exhibit impressive power, benefiting from the
extremely large model capacity and broad training data. However, in practice,
downstream scenarios may only support a small model due to the limited
computational resources or efficiency considerations. Moreover, the data used
for pretraining foundation models are usually invisible and very different from
the target data of downstream tasks. This brings a critical challenge for the
real-world application of foundation models: one has to transfer the knowledge
of a foundation model to the downstream task that has a quite different
architecture with only downstream target data. Existing transfer learning or
knowledge distillation methods depend on either the same model structure or
finetuning of the foundation model. Thus, naively introducing these methods can
be either infeasible or very inefficient. To address this, we propose a
Task-Driven Model Reprogramming (TDMR) framework. Specifically, we reprogram
the foundation model to project the knowledge into a proxy space, which
alleviates the adverse effect of task mismatch and domain inconsistency. Then,
we reprogram the target model via progressive distillation from the proxy space
to efficiently learn the knowledge from the reprogrammed foundation model. TDMR
is compatible with different pre-trained model types (CNN, transformer or their
mix) and limited target data, and promotes the wide applications of vision
foundation models to downstream tasks in a cost-effective manner. Extensive
experiments on different downstream classification tasks and target model
structures demonstrate the effectiveness of our methods with both CNNs and
transformer foundation models.

该论文提出了一种基于任务的模型重新编程框架，可用于模型之间的知识跨越传递，从而便于在 Downstream 任务中使用视觉基础模型，实现向下游任务的低成本迁移。该方法适用于不同类型的预训练模型（CNN、Transformer 或其混合），并证明了在不同的下游分类任务和目标模型结构上的有效性。