Effective object detection in mobile robots is challenged by deployment in
diverse and unfamiliar environments. Online Source-Free Domain Adaptation
(O-SFDA) offers real-time model adaptation using a stream of unlabeled data
from a target domain. However, not all captured frames in mobile robotics
contain information that is beneficial for adaptation, particularly when there
is a strong domain shift. This paper introduces a novel approach to enhance
O-SFDA for adaptive object detection in mobile robots via unsupervised data
acquisition. Our methodology prioritizes the most informative unlabeled samples
for inclusion in the online training process. Empirical evaluation on a
real-world dataset reveals that our method outperforms existing
state-of-the-art O-SFDA techniques, demonstrating the viability of unsupervised
data acquisition for improving adaptive object detection in mobile robots.

移动机器人中的目标检测的有效性受到多样和陌生环境的挑战。本文通过无监督数据采集引入一种增强移动机器人中自适应目标检测的新方法，该方法以在线无源域自适应为基础，通过优先选择最具信息量的未标记样本来改进检测性能，并经过实证评估，结果显示我们的方法优于现有最先进的方法，证明了无监督数据采集在提高移动机器人自适应目标检测方面的可行性。

通过无监督数据获得改进的在线无源领域自适应的目标检测方法

Improving Online Source-free Domain Adaptation for Object Detection by  Unsupervised Data Acquisition

Over the years various methods have been proposed for the problem of object
detection. Recently, we have witnessed great strides in this domain owing to
the emergence of powerful deep neural networks. However, there are typically
two main assumptions common among these approaches. First, the model is trained
on a fixed training set and is evaluated on a pre-recorded test set. Second,
the model is kept frozen after the training phase, so no further updates are
performed after the training is finished. These two assumptions limit the
applicability of these methods to real-world settings. In this paper, we
propose Interactron, a method for adaptive object detection in an interactive
setting, where the goal is to perform object detection in images observed by an
embodied agent navigating in different environments. Our idea is to continue
training during inference and adapt the model at test time without any explicit
supervision via interacting with the environment. Our adaptive object detection
model provides a 7.2 point improvement in AP (and 12.7 points in AP50) over
DETR, a recent, high-performance object detector. Moreover, we show that our
object detection model adapts to environments with completely different
appearance characteristics, and performs well in them. The code is available
at: this https URL .

在这篇论文中，我们提出了 Interactron 这种方法，它可以在交互式环境中进行自适应目标检测，通过与环境进行交互，在推理过程中继续训练并适应模型。我们的自适应目标检测模型相对于高性能目标检测器 DETR 获得了 7.2 个点的 AP（和 12.7 个点的 AP50）改进，在完全不同的外观特征环境中表现良好。

Interactron：具身自适应物体检测

Interactron: Embodied Adaptive Object Detection

Recent advances in adaptive object detection have achieved compelling results
in virtue of adversarial feature adaptation to mitigate the distributional
shifts along the detection pipeline. Whilst adversarial adaptation
significantly enhances the transferability of feature representations, the
feature discriminability of object detectors remains less investigated.
Moreover, transferability and discriminability may come at a contradiction in
adversarial adaptation given the complex combinations of objects and the
differentiated scene layouts between domains. In this paper, we propose a
Hierarchical Transferability Calibration Network (HTCN) that hierarchically
(local-region/image/instance) calibrates the transferability of feature
representations for harmonizing transferability and discriminability. The
proposed model consists of three components: (1) Importance Weighted
Adversarial Training with input Interpolation (IWAT-I), which strengthens the
global discriminability by re-weighting the interpolated image-level features;
(2) Context-aware Instance-Level Alignment (CILA) module, which enhances the
local discriminability by capturing the underlying complementary effect between
the instance-level feature and the global context information for the
instance-level feature alignment; (3) local feature masks that calibrate the
local transferability to provide semantic guidance for the following
discriminative pattern alignment. Experimental results show that HTCN
significantly outperforms the state-of-the-art methods on benchmark datasets.

本文提出了一种基于 Hierarchical Transferability Calibration Network 的自适应目标检测方法，其中采用各种技术（如输入插值、自适应训练、上下文感知实例层对齐）来提高特征可辨别性和可转移性，从而使得该方法在目标检测领域表现出色。