Self-supervised learning (SSL) has been investigated to generate
task-agnostic representations across various domains. However, such
investigation has not been conducted for detecting multiple mental disorders.
The rationale behind the existence of a task-agnostic representation lies in
the overlapping symptoms among multiple mental disorders. Consequently, the
behavioural data collected for mental health assessment may carry a mixed bag
of attributes related to multiple disorders. Motivated by that, in this study,
we explore a task-agnostic representation derived through SSL in the context of
detecting major depressive disorder (MDD) and post-traumatic stress disorder
(PTSD) using audio and video data collected during interactive sessions. This
study employs SSL models trained by predicting multiple fixed targets or masked
frames. We propose a list of fixed targets to make the generated representation
more efficient for detecting MDD and PTSD. Furthermore, we modify the
hyper-parameters of the SSL encoder predicting fixed targets to generate global
representations that capture varying temporal contexts. Both these innovations
are noted to yield improved detection performances for considered mental
disorders and exhibit task-agnostic traits. In the context of the SSL model
predicting masked frames, the generated global representations are also noted
to exhibit task-agnostic traits.

通过 SSL 在检测抑郁症和创伤后应激障碍时使用音频和视频数据所生成的任务不可知的表示具有改进的检测性能和任务不可知的特性。

探索自监督学习的任务无关特性在检测心理障碍中的应用

Exploring the Task-agnostic Trait of Self-supervised Learning in the  Context of Detecting Mental Disorders

In this work, we present an approach to construct a video-based robot policy
capable of reliably executing diverse tasks across different robots and
environments from few video demonstrations without using any action
annotations. Our method leverages images as a task-agnostic representation,
encoding both the state and action information, and text as a general
representation for specifying robot goals. By synthesizing videos that
``hallucinate'' robot executing actions and in combination with dense
correspondences between frames, our approach can infer the closed-formed action
to execute to an environment without the need of any explicit action labels.
This unique capability allows us to train the policy solely based on RGB videos
and deploy learned policies to various robotic tasks. We demonstrate the
efficacy of our approach in learning policies on table-top manipulation and
navigation tasks. Additionally, we contribute an open-source framework for
efficient video modeling, enabling the training of high-fidelity policy models
with four GPUs within a single day.

通过使用图像作为任务非特定的表示，编码状态和动作信息，以及使用文本作为设置机器人目标的一般表示，我们提出了一种构建视频机器人策略的方法，可以在没有使用任何动作注释的情况下，从少量视频示例中可靠地执行不同机器人和环境中的各种任务。