Scene graph generation (SGG) involves analyzing images to extract meaningful
information about objects and their relationships. Given the dynamic nature of
the visual world, it becomes crucial for AI systems to detect new objects and
establish their new relationships with existing objects. To address the lack of
continual learning methodologies in SGG, we introduce the comprehensive
Continual ScenE Graph Generation (CSEGG) dataset along with 3 learning
scenarios and 8 evaluation metrics. Our research investigates the continual
learning performances of existing SGG methods on the retention of previous
object entities and relationships as they learn new ones. Moreover, we also
explore how continual object detection enhances generalization in classifying
known relationships on unknown objects. We conduct extensive experiments
benchmarking and analyzing the classical two-stage SGG methods and the most
recent transformer-based SGG methods in continual learning settings, and gain
valuable insights into the CSEGG problem. We invite the research community to
explore this emerging field of study.

通过引入包含三个学习场景和八个评估指标的综合连续场景图生成（CSEGG）数据集，研究深入探索现有的 SGG 方法在学习新对象时对先前对象实体和关系的保留情况，以及连续目标检测如何增强对未知对象上已知关系的泛化性能。

自适应视觉场景理解：增量场景图生成

Adaptive Visual Scene Understanding: Incremental Scene Graph Generation

Face clustering plays an essential role in exploiting massive unlabeled face
data. Recently, graph-based face clustering methods are getting popular for
their satisfying performances. However, they usually suffer from excessive
memory consumption especially on large-scale graphs, and rely on empirical
thresholds to determine the connectivities between samples in inference, which
restricts their applications in various real-world scenes. To address such
problems, in this paper, we explore face clustering from the pairwise angle.
Specifically, we formulate the face clustering task as a pairwise relationship
classification task, avoiding the memory-consuming learning on large-scale
graphs. The classifier can directly determine the relationship between samples
and is enhanced by taking advantage of the contextual information. Moreover, to
further facilitate the efficiency of our method, we propose a rank-weighted
density to guide the selection of pairs sent to the classifier. Experimental
results demonstrate that our method achieves state-of-the-art performances on
several public clustering benchmarks at the fastest speed and shows a great
advantage in comparison with graph-based clustering methods on memory
consumption.

本文介绍了一种新的人脸聚类方法，将人脸聚类任务转化为成对关系分类任务，避免了在大规模图上训练所需的高内存消耗，并借助上下文信息以增强分类器性能。同时，引入排名加权密度来指导选择输入分类器的对。实验结果表明，该方法速度最快，内存消耗最小，并且在几个公共聚类基准上达到了最先进的性能。

通过二元分类学习人脸聚类

Learn to Cluster Faces via Pairwise Classification

This paper strives to classify and detect the relationship between object
tubelets appearing within a video as a <subject-predicate-object> triplet.
Where existing works treat object proposals or tubelets as single entities and
model their relations a posteriori, we propose to classify and detect
predicates for pairs of object tubelets a priori. We also propose Social
Fabric: an encoding that represents a pair of object tubelets as a composition
of interaction primitives. These primitives are learned over all relations,
resulting in a compact representation able to localize and classify relations
from the pool of co-occurring object tubelets across all timespans in a video.
The encoding enables our two-stage network. In the first stage, we train Social
Fabric to suggest proposals that are likely interacting. We use the Social
Fabric in the second stage to simultaneously fine-tune and predict predicate
labels for the tubelets. Experiments demonstrate the benefit of early video
relation modeling, our encoding and the two-stage architecture, leading to a
new state-of-the-art on two benchmarks. We also show how the encoding enables
query-by-primitive-example to search for spatio-temporal video relations. Code:
this https URL

本文提出了一种通过分类和检测视频中的对象 tubelets 之间的关系来实现主语 - 谓语 - 宾语三元组的方法，通过学习所有关系中的交互原语来紧凑地表示对象 tubelets 对的编码，并展示了其在两个基准测试上的最新成果，同时展示了如何使用编码实现按原语示例查询时空关系。