Phytoplankton, a crucial component of aquatic ecosystems, requires efficient
monitoring to understand marine ecological processes and environmental
conditions. Traditional phytoplankton monitoring methods, relying on non-in
situ observations, are time-consuming and resource-intensive, limiting timely
analysis. To address these limitations, we introduce PhyTracker, an intelligent
in situ tracking framework designed for automatic tracking of phytoplankton.
PhyTracker overcomes significant challenges unique to phytoplankton monitoring,
such as constrained mobility within water flow, inconspicuous appearance, and
the presence of impurities. Our method incorporates three innovative modules: a
Texture-enhanced Feature Extraction (TFE) module, an Attention-enhanced
Temporal Association (ATA) module, and a Flow-agnostic Movement Refinement
(FMR) module. These modules enhance feature capture, differentiate between
phytoplankton and impurities, and refine movement characteristics,
respectively. Extensive experiments on the PMOT dataset validate the
superiority of PhyTracker in phytoplankton tracking, and additional tests on
the MOT dataset demonstrate its general applicability, outperforming
conventional tracking methods. This work highlights key differences between
phytoplankton and traditional objects, offering an effective solution for
phytoplankton monitoring.

PhyTracker 是一种智能的现场追踪框架，旨在通过三个创新模块（纹理增强特征提取模块、注意力增强时间关联模块和流无关的运动细化模块）解决水中浮游植物监测的挑战，并经过广泛的实验验证了其在浮游植物跟踪方面的优越性。

PhyTracker：一种适用于浮游植物的在线跟踪器

PhyTracker: An Online Tracker for Phytoplankton

Extracting medical knowledge from healthcare texts enhances downstream tasks
like medical knowledge graph construction and clinical decision-making.
However, the construction and application of knowledge extraction models lack
automation, reusability and unified management, leading to inefficiencies for
researchers and high barriers for non-AI experts such as doctors, to utilize
knowledge extraction. To address these issues, we propose a ModelOps-based
intelligent medical knowledge extraction framework that offers a low-code
system for model selection, training, evaluation and optimization.
Specifically, the framework includes a dataset abstraction mechanism based on
multi-layer callback functions, a reusable model training, monitoring and
management mechanism. We also propose a model recommendation method based on
dataset similarity, which helps users quickly find potentially suitable models
for a given dataset. Our framework provides convenience for researchers to
develop models and simplifies model access for non-AI experts such as doctors.

基于 ModelOps 的智能医学知识提取框架，通过多层回调函数的数据集抽象机制、可重用的模型训练、监控和管理机制，以及基于数据集相似性的模型推荐方法，提供了低代码系统，方便医学研究者开发模型，并简化了非人工智能专家如医生对模型的访问。