Out-of-distribution (OOD) detection aims to detect "unknown" data whose
labels have not been seen during the in-distribution (ID) training process.
Recent progress in representation learning gives rise to distance-based OOD
detection that recognizes inputs as ID/OOD according to their relative
distances to the training data of ID classes. Previous approaches calculate
pairwise distances relying only on global image representations, which can be
sub-optimal as the inevitable background clutter and intra-class variation may
drive image-level representations from the same ID class far apart in a given
representation space. In this work, we overcome this challenge by proposing
Multi-scale OOD DEtection (MODE), a first framework leveraging both global
visual information and local region details of images to maximally benefit OOD
detection. Specifically, we first find that existing models pretrained by
off-the-shelf cross-entropy or contrastive losses are incompetent to capture
valuable local representations for MODE, due to the scale-discrepancy between
the ID training and OOD detection processes. To mitigate this issue and
encourage locally discriminative representations in ID training, we propose
Attention-based Local PropAgation (ALPA), a trainable objective that exploits a
cross-attention mechanism to align and highlight the local regions of the
target objects for pairwise examples. During test-time OOD detection, a
Cross-Scale Decision (CSD) function is further devised on the most
discriminative multi-scale representations to distinguish ID/OOD data more
faithfully. We demonstrate the effectiveness and flexibility of MODE on several
benchmarks -- on average, MODE outperforms the previous state-of-the-art by up
to 19.24% in FPR, 2.77% in AUROC. Code is available at
this https URL

本研究提出了第一个多尺度的 “模式”（MODE）框架，通过同时利用图像的全局视觉信息和局部区域细节来实现最大效益的外域检测。通过引入注意力机制和交叉尺度决策函数，MODE 在几个基准测试中表现出卓越的性能，平均可以在误报率上提高 19.24%、在 AUROC 上提高 2.77%。

从全球到本地：多尺度的离群检测

From Global to Local: Multi-scale Out-of-distribution Detection

Gait recognition is one of the most important biometric technologies and has
been applied in many fields. Recent gait recognition frameworks represent each
gait frame by descriptors extracted from either global appearances or local
regions of humans. However, the representations based on global information
often neglect the details of the gait frame, while local region based
descriptors cannot capture the relations among neighboring regions, thus
reducing their discriminativeness. In this paper, we propose a novel feature
extraction and fusion framework to achieve discriminative feature
representations for gait recognition. Towards this goal, we take advantage of
both global visual information and local region details and develop a Global
and Local Feature Extractor (GLFE). Specifically, our GLFE module is composed
of our newly designed multiple global and local convolutional layers (GLConv)
to ensemble global and local features in a principle manner. Furthermore, we
present a novel operation, namely Local Temporal Aggregation (LTA), to further
preserve the spatial information by reducing the temporal resolution to obtain
higher spatial resolution. With the help of our GLFE and LTA, our method
significantly improves the discriminativeness of our visual features, thus
improving the gait recognition performance. Extensive experiments demonstrate
that our proposed method outperforms state-of-the-art gait recognition methods
on two popular datasets.

本文提出了一种通过全局可视信息和局部区域细节来实现步态识别的辨别性特征提取和融合框架，使用多个全局和局部卷积层来组合全局和局部特征，并提出了本地时间聚合操作来进一步保留空间信息以获得更高的空间分辨率。 实验结果表明，该方法在两个流行数据集上优于现有技术。