In this work, we address the challenging and emergent problem of novel object
detection (NOD), focusing on the accurate detection of both known and novel
object categories during inference. Traditional object detection algorithms are
inherently closed-set, limiting their capability to handle NOD. We present a
novel approach to transform existing closed-set detectors into open-set
detectors. This transformation is achieved by leveraging the complementary
strengths of pre-trained foundational models, specifically CLIP and SAM,
through our cooperative mechanism. Furthermore, by integrating this mechanism
with state-of-the-art open-set detectors such as GDINO, we establish new
benchmarks in object detection performance. Our method achieves 17.42 mAP in
novel object detection and 42.08 mAP for known objects on the challenging LVIS
dataset. Adapting our approach to the COCO OVD split, we surpass the current
state-of-the-art by a margin of 7.2 $ \text{AP}_{50} $ for novel classes. Our
code is available at
this https URL .

我们提出了一种新颖的方法将现有的闭集检测器转化为开集检测器，并通过将 CLIP、SAM 和 GDINO 等模型进行协同来建立新的目标检测性能基准，实现对新颖对象和已知对象的准确检测。

通过合作的基础模型增强新颖物体检测

Enhancing Novel Object Detection via Cooperative Foundational Models

This paper proposes a cooperative mechanism for mitigating the performance
degradation due to non-independent-and-identically-distributed (non-IID) data
in collaborative machine learning (ML), namely federated learning (FL), which
trains an ML model using the rich data and computational resources of mobile
clients without gathering their data to central systems. The data of mobile
clients is typically non-IID owing to diversity among mobile clients' interests
and usage, and FL with non-IID data could degrade the model performance.
Therefore, to mitigate the degradation induced by non-IID data, we assume that
a limited number (e.g., less than 1%) of clients allow their data to be
uploaded to a server, and we propose a hybrid learning mechanism referred to as
Hybrid-FL, wherein the server updates the model using the data gathered from
the clients and aggregates the model with the models trained by clients. The
Hybrid-FL solves both client- and data-selection problems via heuristic
algorithms, which try to select the optimal sets of clients who train models
with their own data, clients who upload their data to the server, and data
uploaded to the server. The algorithms increase the number of clients
participating in FL and make more data gather in the server IID, thereby
improving the prediction accuracy of the aggregated model. Evaluations, which
consist of network simulations and ML experiments, demonstrate that the
proposed scheme achieves a 13.5% higher classification accuracy than those of
the previously proposed schemes for the non-IID case.

提出了一种名为 Hybrid-FL 的协作机制，通过启发式算法解决数据和客户端选择问题，从而缓解非独立同分布数据带来的弊端，在网络模拟和机器学习实验中证明了该方案比之前提出的方案在非 IID 情况下具有更高的分类准确率。

无线网络的混合联邦学习：使用非独立同分布数据的合作学习机制

Hybrid-FL for Wireless Networks: Cooperative Learning Mechanism Using  Non-IID Data

Resource balancing within complex transportation networks is one of the most
important problems in real logistics domain. Traditional solutions on these
problems leverage combinatorial optimization with demand and supply
forecasting. However, the high complexity of transportation routes, severe
uncertainty of future demand and supply, together with non-convex business
constraints make it extremely challenging in the traditional resource
management field. In this paper, we propose a novel sophisticated multi-agent
reinforcement learning approach to address these challenges. In particular,
inspired by the externalities especially the interactions among resource
agents, we introduce an innovative cooperative mechanism for state and reward
design resulting in more effective and efficient transportation. Extensive
experiments on a simulated ocean transportation service demonstrate that our
new approach can stimulate cooperation among agents and lead to much better
performance. Compared with traditional solutions based on combinatorial
optimization, our approach can give rise to a significant improvement in terms
of both performance and stability.

本篇论文提出了一种基于多智能体强化学习的新型协作机制以及一种创新的状态和奖励设计，对复杂交通网络中的资源平衡问题进行优化，实验结果表明，该方法能够更好地激励代理之间的合作，相比于传统的基于组合优化的方法，可以显著提高交通服务的效果和稳定性。