Few-shot segmentation models excel in metal defect detection due to their
rapid generalization ability to new classes and pixel-level segmentation,
rendering them ideal for addressing data scarcity issues and achieving refined
object delineation in industrial applications. Existing works neglect the
\textit{Intra-Class Differences}, inherent in metal surface defect data, which
hinders the model from learning sufficient knowledge from the support set to
guide the query set segmentation. Specifically, it can be categorized into two
types: the \textit{Semantic Difference} induced by internal factors in metal
samples and the \textit{Distortion Difference} caused by external factors of
surroundings. To address these differences, we introduce a \textbf{L}ocal
d\textbf{E}scriptor based \textbf{R}easoning and \textbf{E}xcitation
\textbf{Net}work (\textbf{LERENet}) to learn the two-view guidance, i.e., local
and global information from the graph and feature space, and fuse them to
segment precisely. Since the relation structure of local features embedded in
graph space will help to eliminate \textit{Semantic Difference}, we employ
Multi-Prototype Reasoning (MPR) module, extracting local descriptors based
prototypes and analyzing local-view feature relevance in support-query pairs.
Besides, due to the global information that will assist in countering the
\textit{Distortion Difference} in observations, we utilize Multi-Prototype
Excitation (MPE) module to capture the global-view relations in support-query
pairs. Finally, we employ an Information Fusion Module (IFM) to fuse learned
prototypes in local and global views to generate pixel-level masks. Our
comprehensive experiments on defect datasets demonstrate that it outperforms
existing benchmarks, establishing a new state-of-the-art.

基于本地描述符和信息融合模块的少样本分割模型用于金属缺陷检测，提高数据稀缺问题与工业应用中的对象分割精度，比现有基准方法表现更好。

LERENet：金属表面缺陷少样本语义分割中消除类内差异

LERENet: Eliminating Intra-class Differences for Metal Surface Defect  Few-shot Semantic Segmentation

Few-shot image classification has emerged as a key challenge in the field of
computer vision, highlighting the capability to rapidly adapt to new tasks with
minimal labeled data. Existing methods predominantly rely on image-level
features or local descriptors, often overlooking the holistic context
surrounding these descriptors. In this work, we introduce a novel approach
termed "Local Descriptor with Contextual Augmentation (LDCA)". Specifically,
this method bridges the gap between local and global understanding uniquely by
leveraging an adaptive global contextual enhancement module. This module
incorporates a visual transformer, endowing local descriptors with contextual
awareness capabilities, ranging from broad global perspectives to intricate
surrounding nuances. By doing so, LDCA transcends traditional descriptor-based
approaches, ensuring each local feature is interpreted within its larger visual
narrative. Extensive experiments underscore the efficacy of our method, showing
a maximal absolute improvement of 20\% over the next-best on fine-grained
classification datasets, thus demonstrating significant advancements in
few-shot classification tasks.

本研究提出了一种名为 “局部描述符上下文增强” 的新方法，通过利用自适应全局上下文增强模块，独特地将局部描素与全局理解之间的差距相互联系起来，从而确保对于每个局部特征都进行更大范围的视觉解释，以取得在细粒度分类任务上最高绝对改进率达 20% 的显著进展。

LDCA: 基于上下文增强的局部描述符的少样本学习

LDCA: Local Descriptors with Contextual Augmentation for Few-Shot  Learning

We present a new local descriptor for 3D shapes, directly applicable to a
wide range of shape analysis problems such as point correspondences, semantic
segmentation, affordance prediction, and shape-to-scan matching. The descriptor
is produced by a convolutional network that is trained to embed geometrically
and semantically similar points close to one another in descriptor space. The
network processes surface neighborhoods around points on a shape that are
captured at multiple scales by a succession of progressively zoomed out views,
taken from carefully selected camera positions. We leverage two extremely large
sources of data to train our network. First, since our network processes
rendered views in the form of 2D images, we repurpose architectures pre-trained
on massive image datasets. Second, we automatically generate a synthetic dense
point correspondence dataset by non-rigid alignment of corresponding shape
parts in a large collection of segmented 3D models. As a result of these design
choices, our network effectively encodes multi-scale local context and
fine-grained surface detail. Our network can be trained to produce either
category-specific descriptors or more generic descriptors by learning from
multiple shape categories. Once trained, at test time, the network extracts
local descriptors for shapes without requiring any part segmentation as input.
Our method can produce effective local descriptors even for shapes whose
category is unknown or different from the ones used while training. We
demonstrate through several experiments that our learned local descriptors are
more discriminative compared to state of the art alternatives, and are
effective in a variety of shape analysis applications.

本文提出了一个新的适用于 3D 形状的本地描述符，通过卷积网络直接从多个尺度的从粗到精细视角来捕捉图像的空间局部结构和表面细节，该技术可用于点对应，语义分割和形状匹配等形状分析领域。