Our research focuses on the analysis and improvement of the Graph-based
Relation Inference Transformer (GRIT), which serves as an important benchmark
in the field. We conduct a comprehensive ablation study using the PISC-fine
dataset, to find and explore improvement in efficiency and performance of
GRITv2. Our research has provided a new state-of-the-art relation recognition
model on the PISC relation dataset. We introduce several features in the GRIT
model and analyse our new benchmarks in two versions: GRITv2-L (large) and
GRITv2-S (small). Our proposed GRITv2-L surpasses existing methods on relation
recognition and the GRITv2-S is within 2% performance gap of GRITv2-L, which
has only 0.0625x the model size and parameters of GRITv2-L. Furthermore, we
also address the need for model compression, an area crucial for deploying
efficient models on resource-constrained platforms. By applying quantization
techniques, we efficiently reduced the GRITv2-S size to 22MB and deployed it on
the flagship OnePlus 12 mobile which still surpasses the PISC-fine benchmarks
in performance, highlighting the practical viability and improved efficiency of
our model on mobile devices.

我们的研究专注于分析和改进基于图的关系推理变形器（GRIT），这是该领域的一个重要基准。我们使用 PISC-fine 数据集进行了全面的消融研究，以寻找和探索 GRITv2 的效率和性能改进。我们的研究在 PISC 关系数据集上提供了一个新的冠军关系识别模型。我们在 GRIT 模型中引入了几个特性，并对我们的新基准进行了分析，分别为 GRITv2-L（大型）和 GRITv2-S（小型）。我们提出的 GRITv2-L 在关系识别上超过了现有方法，而 GRITv2-S 与 GRITv2-L 的性能差距仅为 2%，而其模型大小和参数仅为 GRITv2-L 的 0.0625 倍。此外，我们还解决了模型压缩的需求，这是在资源受限平台上部署高效模型的关键领域。通过应用量化技术，我们将 GRITv2-S 的大小有效地减小到 22MB，并在旗舰手机 OnePlus 12 上部署，仍然超越 PISC-fine 基准，突显了我们的模型在移动设备上的实用性和改进的高效性。

GRITv2：高效轻量级社交关系识别

GRITv2: Efficient and Light-weight Social Relation Recognition

A discourse containing one or more sentences describes daily issues and
events for people to communicate their thoughts and opinions. As sentences are
normally consist of multiple text segments, correct understanding of the theme
of a discourse should take into consideration of the relations in between text
segments. Although sometimes a connective exists in raw texts for conveying
relations, it is more often the cases that no connective exists in between two
text segments but some implicit relation does exist in between them. The task
of implicit discourse relation recognition (IDRR) is to detect implicit
relation and classify its sense between two text segments without a connective.
Indeed, the IDRR task is important to diverse downstream natural language
processing tasks, such as text summarization, machine translation and so on.
This article provides a comprehensive and up-to-date survey for the IDRR task.
We first summarize the task definition and data sources widely used in the
field. We categorize the main solution approaches for the IDRR task from the
viewpoint of its development history. In each solution category, we present and
analyze the most representative methods, including their origins, ideas,
strengths and weaknesses. We also present performance comparisons for those
solutions experimented on a public corpus with standard data processing
procedures. Finally, we discuss future research directions for discourse
relation analysis.

本调查研究提供了隐含话语关系识别（IDRR）任务的全面和最新发展情况，包括任务定义、数据源、解决方案及其性能比较，并讨论了该领域的未来研究方向。

隐含式篇章关系识别调查

A Survey of Implicit Discourse Relation Recognition

This paper deals with a challenging task of video scene graph generation
(VidSGG), which could serve as a structured video representation for high-level
understanding tasks. We present a new {\em detect-to-track} paradigm for this
task by decoupling the context modeling for relation prediction from the
complicated low-level entity tracking. Specifically, we design an efficient
method for frame-level VidSGG, termed as {\em Target Adaptive Context
Aggregation Network} (TRACE), with a focus on capturing spatio-temporal context
information for relation recognition. Our TRACE framework streamlines the
VidSGG pipeline with a modular design, and presents two unique blocks of
Hierarchical Relation Tree (HRTree) construction and Target-adaptive Context
Aggregation. More specific, our HRTree first provides an adpative structure for
organizing possible relation candidates efficiently, and guides context
aggregation module to effectively capture spatio-temporal structure
information. Then, we obtain a contextualized feature representation for each
relation candidate and build a classification head to recognize its relation
category. Finally, we provide a simple temporal association strategy to track
TRACE detected results to yield the video-level VidSGG. We perform experiments
on two VidSGG benchmarks: ImageNet-VidVRD and Action Genome, and the results
demonstrate that our TRACE achieves the state-of-the-art performance. The code
and models are made available at https://github.com/MCG-NJU/TRACE.

本文提出了一种新的 “检测到跟踪” 范例，使用 Hierarchical Relation Tree 和 Target-adaptive Context Aggregation 分块设计，利用异构的知识从而提高视频情境图生成的效率，并在 ImageNet-VidVRD 和 Action Genome 数据集上展示了良好的表现。