Video anomaly detection aims to develop automated models capable of
identifying abnormal events in surveillance videos. The benchmark setup for
this task is extremely challenging due to: i) the limited size of the training
sets, ii) weak supervision provided in terms of video-level labels, and iii)
intrinsic class imbalance induced by the scarcity of abnormal events. In this
work, we show that distilling knowledge from aggregated representations of
multiple backbones into a relatively simple model achieves state-of-the-art
performance. In particular, we develop a bi-level distillation approach along
with a novel disentangled cross-attention-based feature aggregation network.
Our proposed approach, DAKD (Distilling Aggregated Knowledge with Disentangled
Attention), demonstrates superior performance compared to existing methods
across multiple benchmark datasets. Notably, we achieve significant
improvements of 1.36%, 0.78%, and 7.02% on the UCF-Crime, ShanghaiTech, and
XD-Violence datasets, respectively.

通过将多个骨干模型的聚合表示进行知识蒸馏，我们提出了一种相对简单的模型，达到了最先进的性能。我们的方法在多个基准数据集上取得了显著提高，尤其是在 UCF-Crime、ShanghaiTech 和 XD-Violence 数据集上，分别实现了 1.36％、0.78％和 7.02％的显著改进。

弱监督视频异常检测的聚合知识蒸馏

Distilling Aggregated Knowledge for Weakly-Supervised Video Anomaly  Detection

Visual localization, i.e., camera pose estimation in a known scene, is a core
component of technologies such as autonomous driving and augmented reality.
State-of-the-art localization approaches often rely on image retrieval
techniques for one of two tasks: (1) provide an approximate pose estimate or
(2) determine which parts of the scene are potentially visible in a given query
image. It is common practice to use state-of-the-art image retrieval algorithms
for these tasks. These algorithms are often trained for the goal of retrieving
the same landmark under a large range of viewpoint changes. However, robustness
to viewpoint changes is not necessarily desirable in the context of visual
localization. This paper focuses on understanding the role of image retrieval
for multiple visual localization tasks. We introduce a benchmark setup and
compare state-of-the-art retrieval representations on multiple datasets. We
show that retrieval performance on classical landmark retrieval/recognition
tasks correlates only for some but not all tasks to localization performance.
This indicates a need for retrieval approaches specifically designed for
localization tasks. Our benchmark and evaluation protocols are available at
this https URL

通过针对视觉定位任务设计的检索方法，对知名的图像检索技术在多个数据集上进行了对比，发现经典的标志性目标的检索效果与定位效果并不完全匹配。