The most commonly used method for addressing 3D geometric registration is the
iterative closet-point algorithm, this approach is incremental and prone to
drift over multiple consecutive frames. The Common strategy to address the
drift is the pose graph optimization subsequent to frame-to-frame registration,
incorporating a loop closure process that identifies previously visited places.
In this paper, we explore a framework that replaces traditional geometric
registration and pose graph optimization with a learned model utilizing
hierarchical attention mechanisms and graph neural networks. We propose a
strategy to condense the data flow, preserving essential information required
for the precise estimation of rigid poses. Our results, derived from tests on
the KITTI Odometry dataset, demonstrate a significant improvement in pose
estimation accuracy. This improvement is especially notable in determining
rotational components when compared with results obtained through conventional
multi-way registration via pose graph optimization. The code will be made
available upon completion of the review process.

本文探索了使用分层注意机制和图神经网络的学习模型来替代传统的几何配准和姿态图优化的框架，通过压缩数据流并保留估计刚性姿态所需的基本信息，大大提高了姿态估计的精确性，尤其在旋转组件方面的改进较为明显。

层次注意力和图神经网络：面向无漂移姿势估计

Hierarchical Attention and Graph Neural Networks: Toward Drift-Free Pose  Estimation

Automatic depression detection on Twitter can help individuals privately and
conveniently understand their mental health status in the early stages before
seeing mental health professionals. Most existing black-box-like deep learning
methods for depression detection largely focused on improving classification
performance. However, explaining model decisions is imperative in health
research because decision-making can often be high-stakes and life-and-death.
Reliable automatic diagnosis of mental health problems including depression
should be supported by credible explanations justifying models' predictions. In
this work, we propose a novel explainable model for depression detection on
Twitter. It comprises a novel encoder combining hierarchical attention
mechanisms and feed-forward neural networks. To support psycholinguistic
studies, our model leverages metaphorical concept mappings as input. Thus, it
not only detects depressed individuals, but also identifies features of such
users' tweets and associated metaphor concept mappings.

在 Twitter 上进行自动检测抑郁症能够帮助个人在寻求心理健康专业人员帮助之前，以私密和方便的方式了解自己的心理健康状况。本文提出了一种基于描述性模型的新型抑郁症检测模型，该模型结合了分层注意机制和前馈神经网络，在支持心理语言学研究的同时，其还识别了用户推文的特征和相关的隐喻概念映射。

基于隐喻概念映射的分层注意力网络用于可解释的推特抑郁症检测

Hierarchical Attention Network for Explainable Depression Detection on Twitter Aided by Metaphor Concept Mappings

We present an approach named JSFusion (Joint Sequence Fusion) that can
measure semantic similarity between any pairs of multimodal sequence data (e.g.
a video clip and a language sentence). Our multimodal matching network consists
of two key components. First, the Joint Semantic Tensor composes a dense
pairwise representation of two sequence data into a 3D tensor. Then, the
Convolutional Hierarchical Decoder computes their similarity score by
discovering hidden hierarchical matches between the two sequence modalities.
Both modules leverage hierarchical attention mechanisms that learn to promote
well-matched representation patterns while prune out misaligned ones in a
bottom-up manner. Although the JSFusion is a universal model to be applicable
to any multimodal sequence data, this work focuses on video-language tasks
including multimodal retrieval and video QA. We evaluate the JSFusion model in
three retrieval and VQA tasks in LSMDC, for which our model achieves the best
performance reported so far. We also perform multiple-choice and movie
retrieval tasks for the MSR-VTT dataset, on which our approach outperforms many
state-of-the-art methods.

本文提出了一种名为 JSFusion（联合序列融合）的方法，可以测量多模式序列数据（例如视频剪辑和语言句子）之间的语义相似性，并通过发现两个序列模态之间的隐藏分层匹配来计算它们的相似度得分，该模型适用于多模式检索和视频 QA 等任务，并在多个数据集上超过了现有的许多最先进的方法。