Object detectors often perform poorly on data that differs from their
training set. Domain adaptive object detection (DAOD) methods have recently
demonstrated strong results on addressing this challenge. Unfortunately, we
identify systemic benchmarking pitfalls that call past results into question
and hamper further progress: (a) Overestimation of performance due to
underpowered baselines, (b) Inconsistent implementation practices preventing
transparent comparisons of methods, and (c) Lack of generality due to outdated
backbones and lack of diversity in benchmarks. We address these problems by
introducing: (1) A unified benchmarking and implementation framework, Align and
Distill (ALDI), enabling comparison of DAOD methods and supporting future
development, (2) A fair and modern training and evaluation protocol for DAOD
that addresses benchmarking pitfalls, (3) A new DAOD benchmark dataset,
CFC-DAOD, enabling evaluation on diverse real-world data, and (4) A new method,
ALDI++, that achieves state-of-the-art results by a large margin. ALDI++
outperforms the previous state-of-the-art by +3.5 AP50 on Cityscapes to Foggy
Cityscapes, +5.7 AP50 on Sim10k to Cityscapes (where ours is the only method to
outperform a fair baseline), and +2.0 AP50 on CFC Kenai to Channel. Our
framework, dataset, and state-of-the-art method offer a critical reset for DAOD
and provide a strong foundation for future research. Code and data are
available: this https URL and
this https URL

通过引入统一的基准测试和实施框架 ALDI、公平且现代的培训与评估协议、新的 DAOD 基准数据集 CFC-DAOD 以及 ALDI++ 方法，我们提供了一个关键的重启点用于域自适应物体检测（DAOD）并为未来的研究奠定了坚实的基础。

对齐和提炼：统一和改进领域自适应目标检测

Align and Distill: Unifying and Improving Domain Adaptive Object  Detection

In the last decade, despite rapid advancements in artificial intelligence
(AI) transforming many industry practices, construction largely lags in
adoption. Recently, the emergence and rapid adoption of advanced large language
models (LLM) like OpenAI's GPT, Google's PaLM, and Meta's Llama have shown
great potential and sparked considerable global interest. However, the current
surge lacks a study investigating the opportunities and challenges of
implementing Generative AI (GenAI) in the construction sector, creating a
critical knowledge gap for researchers and practitioners. This underlines the
necessity to explore the prospects and complexities of GenAI integration.
Bridging this gap is fundamental to optimizing GenAI's early-stage adoption
within the construction sector. Given GenAI's unprecedented capabilities to
generate human-like content based on learning from existing content, we reflect
on two guiding questions: What will the future bring for GenAI in the
construction industry? What are the potential opportunities and challenges in
implementing GenAI in the construction industry? This study delves into
reflected perception in literature, analyzes the industry perception using
programming-based word cloud and frequency analysis, and integrates authors'
opinions to answer these questions. This paper recommends a conceptual GenAI
implementation framework, provides practical recommendations, summarizes future
research questions, and builds foundational literature to foster subsequent
research expansion in GenAI within the construction and its allied architecture
& engineering domains.

深入研究了建筑行业中应用生成人工智能（GenAI）的潜在机遇和挑战，提出了概念性的 GenAI 实施框架、实用建议，总结了未来研究的问题，并构建了关于 GenAI 的基础文献。