The task of moment localization is to localize a temporal moment in an
untrimmed video for a given natural language query. Since untrimmed video
contains highly redundant contents, the quality of the query is crucial for
accurately localizing moments, i.e., the query should provide precise
information about the target moment so that the localization model can
understand what to look for in the videos. However, the natural language
queries in current datasets may not be easy to understand for existing models.
For example, the Ego4D dataset uses question sentences as the query to describe
relatively complex moments. While being natural and straightforward for humans,
understanding such question sentences are challenging for mainstream moment
localization models like 2D-TAN. Inspired by the recent success of large
language models, especially their ability of understanding and generating
complex natural language contents, in this extended abstract, we make early
attempts at reformulating the moment queries into a set of instructions using
large language models and making them more friendly to the localization models.

这篇文章讨论了如何利用大型语言模型来改进时刻定位的自然语言查询，使其更适合定位模型处理复杂的未剪辑视频内容。

促使大型语言模型改写查询以定位时刻

Prompting Large Language Models to Reformulate Queries for Moment  Localization

We propose Cut-and-LEaRn (CutLER), a simple approach for training
unsupervised object detection and segmentation models. We leverage the property
of self-supervised models to 'discover' objects without supervision and amplify
it to train a state-of-the-art localization model without any human labels.
CutLER first uses our proposed MaskCut approach to generate coarse masks for
multiple objects in an image and then learns a detector on these masks using
our robust loss function. We further improve the performance by self-training
the model on its predictions. Compared to prior work, CutLER is simpler,
compatible with different detection architectures, and detects multiple
objects. CutLER is also a zero-shot unsupervised detector and improves
detection performance AP50 by over 2.7 times on 11 benchmarks across domains
like video frames, paintings, sketches, etc. With finetuning, CutLER serves as
a low-shot detector surpassing MoCo-v2 by 7.3% APbox and 6.6% APmask on COCO
when training with 5% labels.

提出了一种名为 Cut-and-LEaRn（CutLER）的简单方法，以训练无监督的物体检测和分割模型，它首先使用所提出的 MaskCut 方法生成图像中多个对象的粗略蒙版，然后在这些蒙版上学习检测器，最后通过对模型的预测进行自我训练实现零样本无监督检测，大幅提升检测性能。