We propose a method that integrates supervised extractive and generative
language models for providing supporting evidence of suicide risk in the
CLPsych 2024 shared task. Our approach comprises three steps. Initially, we
construct a BERT-based model for estimating sentence-level suicide risk and
negative sentiment. Next, we precisely identify high suicide risk sentences by
emphasizing elevated probabilities of both suicide risk and negative sentiment.
Finally, we integrate generative summaries using the MentaLLaMa framework and
extractive summaries from identified high suicide risk sentences and a
specialized dictionary of suicidal risk words. SophiaADS, our team, achieved
1st place for highlight extraction and ranked 10th for summary generation, both
based on recall and consistency metrics, respectively.

我们使用监督的抽取和生成式语言模型集成的方法来提供 2024 年 CLPsych 共享任务中对自杀风险的支持证据。我们的方法包括三个步骤：首先，构建一个基于 BERT 的模型来估计句子级的自杀风险和负面情绪；接下来，通过强调自杀风险和负面情绪的高概率，精确识别出高自杀风险的句子；最后，我们使用 MentaLLaMa 框架的生成式摘要和从高自杀风险句子和专门的自杀风险词典中的抽取式摘要进行集成。我们的团队 SophiaADS 在突出摘要方面获得第一名，并在摘要生成方面在基于回收率和一致性度量标准的排名中排名第 10。

整合有监督的抽取式和生成式语言模型进行自杀风险证据摘要

Integrating Supervised Extractive and Generative Language Models for  Suicide Risk Evidence Summarization

We present a method for estimating detailed scene illumination using human
faces in a single image. In contrast to previous works that estimate lighting
in terms of low-order basis functions or distant point lights, our technique
estimates illumination at a higher precision in the form of a non-parametric
environment map. Based on the observation that faces can exhibit strong
highlight reflections from a broad range of lighting directions, we propose a
deep neural network for extracting highlights from faces, and then trace these
reflections back to the scene to acquire the environment map. Since real
training data for highlight extraction is very limited, we introduce an
unsupervised scheme for finetuning the network on real images, based on the
consistent diffuse chromaticity of a given face seen in multiple real images.
In tracing the estimated highlights to the environment, we reduce the blurring
effect of skin reflectance on reflected light through a deconvolution
determined by prior knowledge on face material properties. Comparisons to
previous techniques for highlight extraction and illumination estimation show
the state-of-the-art performance of this approach on a variety of indoor and
outdoor scenes.

本文提出了一种利用单张图片中的人脸来估计场景光照的方法，通过深度神经网络提取高光并追溯回环境中，得到非参数化的环境图，该方法表现出了最新技术水平，适用于各种室内外场景。

利用无监督深度高光提取技术将人脸作为光探针

Faces as Lighting Probes via Unsupervised Deep Highlight Extraction

With the growing popularity of short-form video sharing platforms such as
\em{Instagram} and \em{Vine}, there has been an increasing need for techniques
that automatically extract highlights from video. Whereas prior works have
approached this problem with heuristic rules or supervised learning, we present
an unsupervised learning approach that takes advantage of the abundance of
user-edited videos on social media websites such as YouTube. Based on the idea
that the most significant sub-events within a video class are commonly present
among edited videos while less interesting ones appear less frequently, we
identify the significant sub-events via a robust recurrent auto-encoder trained
on a collection of user-edited videos queried for each particular class of
interest. The auto-encoder is trained using a proposed shrinking exponential
loss function that makes it robust to noise in the web-crawled training data,
and is configured with bidirectional long short term memory
(LSTM)~\cite{LSTM:97} cells to better model the temporal structure of highlight
segments. Different from supervised techniques, our method can infer highlights
using only a set of downloaded edited videos, without also needing their
pre-edited counterparts which are rarely available online. Extensive
experiments indicate the promise of our proposed solution in this challenging
unsupervised settin

该研究提出了一种基于循环自编码器的无监督学习方法，用于从社交媒体的用户编辑视频中提取高光时刻，并通过一种收缩指数损失函数训练了这个自编码器，以使其具有噪声容忍度和更好地建模时间结构。