Semantic segmentation and stereo matching are two essential components of 3D
environmental perception systems for autonomous driving. Nevertheless,
conventional approaches often address these two problems independently,
employing separate models for each task. This approach poses practical
limitations in real-world scenarios, particularly when computational resources
are scarce or real-time performance is imperative. Hence, in this article, we
introduce S$^3$M-Net, a novel joint learning framework developed to perform
semantic segmentation and stereo matching simultaneously. Specifically,
S$^3$M-Net shares the features extracted from RGB images between both tasks,
resulting in an improved overall scene understanding capability. This feature
sharing process is realized using a feature fusion adaption (FFA) module, which
effectively transforms the shared features into semantic space and subsequently
fuses them with the encoded disparity features. The entire joint learning
framework is trained by minimizing a novel semantic consistency-guided (SCG)
loss, which places emphasis on the structural consistency in both tasks.
Extensive experimental results conducted on the vKITTI2 and KITTI datasets
demonstrate the effectiveness of our proposed joint learning framework and its
superior performance compared to other state-of-the-art single-task networks.
Our project webpage is accessible at mias.group/S3M-Net.

本文介绍了一种新的联合学习框架 S^3M-Net，用于同时进行语义分割和立体匹配，通过特征共享和融合来提高整体场景理解能力，通过最小化语义一致性引导损失来训练整个联合学习框架，在 vKITTI2 和 KITTI 数据集上进行实验，证明了该框架的有效性和超越其他单任务网络的性能。

S$^3$M-Net: 自主驾驶中语义分割与立体匹配的联合学习

S$^3$M-Net: Joint Learning of Semantic Segmentation and Stereo Matching  for Autonomous Driving

Event extraction (EE) is a crucial information extraction task that aims to
extract event information in texts. Most existing methods assume that events
appear in sentences without overlaps, which are not applicable to the
complicated overlapping event extraction. This work systematically studies the
realistic event overlapping problem, where a word may serve as triggers with
several types or arguments with different roles. To tackle the above problem,
we propose a novel joint learning framework with cascade decoding for
overlapping event extraction, termed as CasEE. Particularly, CasEE sequentially
performs type detection, trigger extraction and argument extraction, where the
overlapped targets are extracted separately conditioned on the specific former
prediction. All the subtasks are jointly learned in a framework to capture
dependencies among the subtasks. The evaluation on a public event extraction
benchmark FewFC demonstrates that CasEE achieves significant improvements on
overlapping event extraction over previous competitive methods.

本研究提出了一种名为 CasEE 的联合学习框架，用于解决事件提取过程中存在的事件重叠问题，它可以有效地提取具有不同角色的多种类型的事件，同时在 FewFC 数据集上得到了显著的提升。

CasEE: 基于级联解码的重叠事件抽取联合学习框架

CasEE: A Joint Learning Framework with Cascade Decoding for Overlapping  Event Extraction

Although a significant progress has been witnessed in supervised person
re-identification (re-id), it remains challenging to generalize re-id models to
new domains due to the huge domain gaps. Recently, there has been a growing
interest in using unsupervised domain adaptation to address this scalability
issue. Existing methods typically conduct adaptation on the representation
space that contains both id-related and id-unrelated factors, thus inevitably
undermining the adaptation efficacy of id-related features. In this paper, we
seek to improve adaptation by purifying the representation space to be adapted.
To this end, we propose a joint learning framework that disentangles
id-related/unrelated features and enforces adaptation to work on the id-related
feature space exclusively. Our model involves a disentangling module that
encodes cross-domain images into a shared appearance space and two separate
structure spaces, and an adaptation module that performs adversarial alignment
and self-training on the shared appearance space. The two modules are
co-designed to be mutually beneficial. Extensive experiments demonstrate that
the proposed joint learning framework outperforms the state-of-the-art methods
by clear margins.

本文提出了一种联合学习框架，通过提纯表征空间来改善适应性，并使用不同的空间对身份相关 / 不相关特征进行解缠，并在共享外观空间上执行对抗性对齐和自我训练进行适应。实验表明，该框架在未受监督的领域自适应方面胜过现有技术。