Unsupervised visible-infrared person re-identification (USVI-ReID) aims to
match specified people in infrared images to visible images without annotation,
and vice versa. USVI-ReID is a challenging yet under-explored task. Most
existing methods address the USVI-ReID problem using cluster-based contrastive
learning, which simply employs the cluster center as a representation of a
person. However, the cluster center primarily focuses on shared information,
overlooking disparity. To address the problem, we propose a Progressive
Contrastive Learning with Multi-Prototype (PCLMP) method for USVI-ReID. In
brief, we first generate the hard prototype by selecting the sample with the
maximum distance from the cluster center. This hard prototype is used in the
contrastive loss to emphasize disparity. Additionally, instead of rigidly
aligning query images to a specific prototype, we generate the dynamic
prototype by randomly picking samples within a cluster. This dynamic prototype
is used to retain the natural variety of features while reducing instability in
the simultaneous learning of both common and disparate information. Finally, we
introduce a progressive learning strategy to gradually shift the model's
attention towards hard samples, avoiding cluster deterioration. Extensive
experiments conducted on the publicly available SYSU-MM01 and RegDB datasets
validate the effectiveness of the proposed method. PCLMP outperforms the
existing state-of-the-art method with an average mAP improvement of 3.9%. The
source codes will be released.

我们提出了一种渐进式对比学习多原型（PCLMP）方法，用于无监督的可见 - 红外人员再识别（USVI-ReID），通过引入硬原型、动态原型和渐进学习策略，该方法在多个数据集上验证了其有效性，超过了现有的最先进方法，并提供源代码。

多原型逐步对比学习的无监督可见 - 红外人员再识别

Progressive Contrastive Learning with Multi-Prototype for Unsupervised  Visible-Infrared Person Re-identification

Motion planning (MP) is one of the core robotics problems requiring fast
methods for finding a collision-free robot motion path connecting the given
start and goal states. Neural motion planners (NMPs) demonstrate fast
computational speed in finding path solutions but require a huge amount of
expert trajectories for learning, thus adding a significant training
computational load. In contrast, recent advancements have also led to a
physics-informed NMP approach that directly solves the Eikonal equation for
motion planning and does not require expert demonstrations for learning.
However, experiments show that the physics-informed NMP approach performs
poorly in complex environments and lacks scalability in multiple scenarios and
high-dimensional real robot settings. To overcome these limitations, this paper
presents a novel and tractable Eikonal equation formulation and introduces a
new progressive learning strategy to train neural networks without expert data
in complex, cluttered, multiple high-dimensional robot motion planning
scenarios. The results demonstrate that our method outperforms state-of-the-art
traditional MP, data-driven NMP, and physics-informed NMP methods by a
significant margin in terms of computational planning speed, path quality, and
success rates. We also show that our approach scales to multiple complex,
cluttered scenarios and the real robot set up in a narrow passage environment.
The proposed method's videos and code implementations are available at
this https URL

提出了一种新的 Eikonal 方程组合方式和逐步学习策略，用于在复杂、杂乱、多高维机器人运动规划情境中训练神经网络。实现结果表明，该方法在计算规划速度、路径质量和成功率方面均显著优于最先进的传统 MP、数据驱动 NMP 和物理信息 NMP 方法，并且可缩放到多个复杂、杂乱情境以及狭窄通道的实际机器人执行情境。