Multiple clustering has gained significant attention in recent years due to
its potential to reveal multiple hidden structures of data from different
perspectives. The advent of deep multiple clustering techniques has notably
advanced the performance by uncovering complex patterns and relationships
within large datasets. However, a major challenge arises as users often do not
need all the clusterings that algorithms generate, and figuring out the one
needed requires a substantial understanding of each clustering result.
Traditionally, aligning a user's brief keyword of interest with the
corresponding vision components was challenging, but the emergence of
multi-modal and large language models (LLMs) has begun to bridge this gap. In
response, given unlabeled target visual data, we propose Multi-MaP, a novel
method employing a multi-modal proxy learning process. It leverages CLIP
encoders to extract coherent text and image embeddings, with GPT-4 integrating
users' interests to formulate effective textual contexts. Moreover, reference
word constraint and concept-level constraint are designed to learn the optimal
text proxy according to the user's interest. Multi-MaP not only adeptly
captures a user's interest via a keyword but also facilitates identifying
relevant clusterings. Our extensive experiments show that Multi-MaP
consistently outperforms state-of-the-art methods in all benchmark
multi-clustering vision tasks. Our code is available at
this https URL

给定未标记的目标视觉数据，作者提出了一种名为 Multi-MaP 的新方法，它利用多模态代理学习过程，借助 CLIP 编码器提取一致的文本和图像嵌入，GPT-4 整合用户的兴趣来制定有效的文本上下文，并设计了参考词约束和概念级约束以根据用户的兴趣学习最佳的文本代理。该方法不仅能够通过关键词熟练地捕捉用户的兴趣，而且还有助于识别相关的聚类，实验证明 Multi-MaP 在所有基准多聚类视觉任务中始终优于最先进的方法。

面向个性化视觉多重聚类的多模态代理学习

Multi-Modal Proxy Learning Towards Personalized Visual Multiple  Clustering

This paper presents ORB-SLAM3, the first system able to perform visual,
visual-inertial and multi-map SLAM with monocular, stereo and RGB-D cameras,
using pin-hole and fisheye lens models. The first main novelty is a
feature-based tightly-integrated visual-inertial SLAM system that fully relies
on Maximum-a-Posteriori (MAP) estimation, even during the IMU initialization
phase. The result is a system that operates robustly in real-time, in small and
large, indoor and outdoor environments, and is 2 to 5 times more accurate than
previous approaches. The second main novelty is a multiple map system that
relies on a new place recognition method with improved recall. Thanks to it,
ORB-SLAM3 is able to survive to long periods of poor visual information: when
it gets lost, it starts a new map that will be seamlessly merged with previous
maps when revisiting mapped areas. Compared with visual odometry systems that
only use information from the last few seconds, ORB-SLAM3 is the first system
able to reuse in all the algorithm stages all previous information. This allows
to include in bundle adjustment co-visible keyframes, that provide high
parallax observations boosting accuracy, even if they are widely separated in
time or if they come from a previous mapping session. Our experiments show
that, in all sensor configurations, ORB-SLAM3 is as robust as the best systems
available in the literature, and significantly more accurate. Notably, our
stereo-inertial SLAM achieves an average accuracy of 3.6 cm on the EuRoC drone
and 9 mm under quick hand-held motions in the room of TUM-VI dataset, a setting
representative of AR/VR scenarios. For the benefit of the community we make
public the source code.

ORB-SLAM3 是第一个能够使用针孔和鱼眼镜头模型的单目、立体和 RGB-D 相机执行视觉、视觉惯性和多地图 SLAM 的系统，并且具有两个主要的创新点：完全依赖于最大后验概率估计的基于特征的紧耦合视觉惯性 SLAM 系统和使用改进的召回率的新的场所识别方法的多地图系统，该系统在所有的传感器配置中都像文献中最好的系统一样健壮，并且显著提高了精度。