Temporal video grounding (TVG) aims to retrieve the time interval of a
language query from an untrimmed video. A significant challenge in TVG is the
low "Semantic Noise Ratio (SNR)", which results in worse performance with lower
SNR. Prior works have addressed this challenge using sophisticated techniques.
In this paper, we propose a no-frills TVG model that consists of two core
modules, namely multi-scale neighboring attention and zoom-in boundary
detection. The multi-scale neighboring attention restricts each video token to
only aggregate visual contexts from its neighbor, enabling the extraction of
the most distinguishing information with multi-scale feature hierarchies from
high-ratio noises. The zoom-in boundary detection then focuses on local-wise
discrimination of the selected top candidates for fine-grained grounding
adjustment. With an end-to-end training strategy, our model achieves
competitive performance on different TVG benchmarks, while also having the
advantage of faster inference speed and lighter model parameters, thanks to its
lightweight architecture.

提供了一个无花样的 TVG 模型，借助多尺度邻近注意和缩放边界检测两个核心模块，提高了在具有低语义噪声比的情况下提取最能区分信息的能力。结合端到端训练策略，该模型在不同的 TVG 基准上实现了竞争性的性能，同时具有更快的推理速度和较轻量的模型参数。

简洁的时间视频定位：多尺度邻近注意力与放大边界检测

No-frills Temporal Video Grounding: Multi-Scale Neighboring Attention  and Zoom-in Boundary Detection

We present a new approach to learn compressible representations in deep
architectures with an end-to-end training strategy. Our method is based on a
soft (continuous) relaxation of quantization and entropy, which we anneal to
their discrete counterparts throughout training. We showcase this method for
two challenging applications: Image compression and neural network compression.
While these tasks have typically been approached with different methods, our
soft-to-hard quantization approach gives results competitive with the
state-of-the-art for both.

本篇论文提出了一种基于端到端训练策略的学习深层结构中可压缩表示的新方法，此方法基于分段连续函数，将分段函数在训练过程中逐渐退化为其离散化函数。并且作者将该方法应用于图片压缩和神经网络压缩两个具有挑战性的领域，取得了与基于不同方法的最先进技术相当的成果。