This work addresses the task of long-term person re-identification.
Typically, person re-identification assumes that people do not change their
clothes, which limits its applications to short-term scenarios. To overcome
this limitation, we investigate long-term person re-identification, which
considers both clothes-changing and clothes-consistent scenarios. In this
paper, we propose a novel framework that effectively learns and utilizes both
global and local information. The proposed framework consists of three streams:
global, local body part, and head streams. The global and head streams encode
identity-relevant information from an entire image and a cropped image of the
head region, respectively. Both streams encode the most distinct, less
distinct, and average features using the combinations of adversarial erasing,
max pooling, and average pooling. The local body part stream extracts
identity-related information for each body part, allowing it to be compared
with the same body part from another image. Since body part annotations are not
available in re-identification datasets, pseudo-labels are generated using
clustering. These labels are then utilized to train a body part segmentation
head in the local body part stream. The proposed framework is trained by
backpropagating the weighted summation of the identity classification loss, the
pair-based loss, and the pseudo body part segmentation loss. To demonstrate the
effectiveness of the proposed method, we conducted experiments on three
publicly available datasets (Celeb-reID, PRCC, and VC-Clothes). The
experimental results demonstrate that the proposed method outperforms the
previous state-of-the-art method.

本文提出了一个新的框架，有效地学习和利用全局信息和局部信息，解决了长期人物再识别中衣物变换和衣物一致情况的限制，并通过生成伪标签和训练身体部位分割模型来提取身体部位特征，实验证明了该方法优于现有的最先进方法。

利用全局、局部身体部位和头部流增强长期个体重新识别

Enhancing Long-Term Person Re-Identification Using Global, Local Body  Part, and Head Streams

Anomaly detection aims to detect data that do not conform to regular
patterns, and such data is also called outliers. The anomalies to be detected
are often tiny in proportion, containing crucial information, and are suitable
for application scenes like intrusion detection, fraud detection, fault
diagnosis, e-commerce platforms, et al. However, in many realistic scenarios,
only the samples following normal behavior are observed, while we can hardly
obtain any anomaly information. To address such problem, we propose an anomaly
detection method GALDetector which is combined of global and local information
based on observed normal samples. The proposed method can be divided into a
three-stage method. Firstly, the global similar normal scores and the local
sparsity scores of unlabeled samples are computed separately. Secondly,
potential anomaly samples are separated from the unlabeled samples
corresponding to these two scores and corresponding weights are assigned to the
selected samples. Finally, a weighted anomaly detector is trained by loads of
samples, then the detector is utilized to identify else anomalies. To evaluate
the effectiveness of the proposed method, we conducted experiments on three
categories of real-world datasets from diverse domains, and experimental
results show that our method achieves better performance when compared with
other state-of-the-art methods.

本文提出了一种基于观测到的正常样本的全局和局部信息相结合的异常检测方法 GALDetector，该方法通过分离出观察样本中的潜在异常样本并为所选样本分配相应的权重来训练带权重的异常检测器，实验结果表明，该方法在三类实际数据集上的表现比其他现有技术更好。

探索全局和局部信息用于正常样本异常检测

Exploring Global and Local Information for Anomaly Detection with Normal  Samples

Recently, learned image compression methods have outperformed traditional
hand-crafted ones including BPG. One of the keys to this success is learned
entropy models that estimate the probability distribution of the quantized
latent representation. Like other vision tasks, most recent learned entropy
models are based on convolutional neural networks (CNNs). However, CNNs have a
limitation in modeling long-range dependencies due to their nature of local
connectivity, which can be a significant bottleneck in image compression where
reducing spatial redundancy is a key point. To overcome this issue, we propose
a novel entropy model called Information Transformer (Informer) that exploits
both global and local information in a content-dependent manner using an
attention mechanism. Our experiments show that Informer improves
rate--distortion performance over the state-of-the-art methods on the Kodak and
Tecnick datasets without the quadratic computational complexity problem. Our
source code is available at this https URL

通过 attention 机制，我们提出了一种新的信息变换学习熵模型，能更好地处理图像压缩的全局和局部依赖关系，实验证明该模型取得了优于现有技术的率失真性能，并且没有二次计算复杂度问题。