Linguistic information is encoded at varying timescales (subwords, phrases,
etc.) and communicative levels, such as syntax and semantics. Contextualized
embeddings have analogously been found to capture these phenomena at
distinctive layers and frequencies. Leveraging these findings, we develop a
fully learnable frequency filter to identify spectral profiles for any given
task. It enables vastly more granular analyses than prior handcrafted filters,
and improves on efficiency. After demonstrating the informativeness of spectral
probing over manual filters in a monolingual setting, we investigate its
multilingual characteristics across seven diverse NLP tasks in six languages.
Our analyses identify distinctive spectral profiles which quantify cross-task
similarity in a linguistically intuitive manner, while remaining consistent
across languages-highlighting their potential as robust, lightweight task
descriptors.

通过开发可完全学习的频率过滤器，我们可以在不同层次和频率上捕捉语言信息。在单语言环境下，我们证明了频谱探测比手工滤波器具有更高的信息量，并在六种语言中的七个 NLP 任务中进行了多语言分析。这些分析鉴定了独特的频谱轮廓，是一种语言直观的方法，而且跨语言是一致的，并展示了其作为强大而轻量级任务描述符的潜力。

光谱探测

Spectral Probing

Underwater images suffer from color distortion and low contrast, because
light is attenuated while it propagates through water. Attenuation under water
varies with wavelength, unlike terrestrial images where attenuation is assumed
to be spectrally uniform. The attenuation depends both on the water body and
the 3D structure of the scene, making color restoration difficult.
Unlike existing single underwater image enhancement techniques, our method
takes into account multiple spectral profiles of different water types. By
estimating just two additional global parameters: the attenuation ratios of the
blue-red and blue-green color channels, the problem is reduced to single image
dehazing, where all color channels have the same attenuation coefficients.
Since the water type is unknown, we evaluate different parameters out of an
existing library of water types. Each type leads to a different restored image
and the best result is automatically chosen based on color distribution.
We collected a dataset of images taken in different locations with varying
water properties, showing color charts in the scenes. Moreover, to obtain
ground truth, the 3D structure of the scene was calculated based on stereo
imaging. This dataset enables a quantitative evaluation of restoration
algorithms on natural images and shows the advantage of our method.

本文提出了一种针对水下图像色彩恢复问题的改进方法，通过考虑不同水类型的多个光谱特征并估计两个额外的全局参数，实现了单图像去雾处理。通过在包含不同水性质的场景中采集图像并计算 3D 结构进行量化评估，结果表明该方法具有较好的还原效果。