In this work, we tackle the problem of domain generalization for object
detection, specifically focusing on the scenario where only a single source
domain is available. We propose an effective approach that involves two key
steps: diversifying the source domain and aligning detections based on class
prediction confidence and localization. Firstly, we demonstrate that by
carefully selecting a set of augmentations, a base detector can outperform
existing methods for single domain generalization by a good margin. This
highlights the importance of domain diversification in improving the
performance of object detectors. Secondly, we introduce a method to align
detections from multiple views, considering both classification and
localization outputs. This alignment procedure leads to better generalized and
well-calibrated object detector models, which are crucial for accurate
decision-making in safety-critical applications. Our approach is
detector-agnostic and can be seamlessly applied to both single-stage and
two-stage detectors. To validate the effectiveness of our proposed methods, we
conduct extensive experiments and ablations on challenging domain-shift
scenarios. The results consistently demonstrate the superiority of our approach
compared to existing methods. Our code and models are available at:
this https URL

解决目标检测的领域泛化问题，通过多样化源域和基于类别预测置信度和定位的对齐检测，并在安全关键应用中做出准确决策。

提升单一领域通用目标检测：注意多样化和对齐

Improving Single Domain-Generalized Object Detection: A Focus on  Diversification and Alignment

This work addresses the cross-corpora generalization issue for the
low-resourced spoken language identification (LID) problem. We have conducted
the experiments in the context of Indian LID and identified strikingly poor
cross-corpora generalization due to corpora-dependent non-lingual biases. Our
contribution to this work is twofold. First, we propose domain diversification,
which diversifies the limited training data using different audio data
augmentation methods. We then propose the concept of maximally diversity-aware
cascaded augmentations and optimize the augmentation fold-factor for effective
diversification of the training data. Second, we introduce the idea of domain
generalization considering the augmentation methods as pseudo-domains. Towards
this, we investigate both domain-invariant and domain-aware approaches. Our LID
system is based on the state-of-the-art emphasized channel attention,
propagation, and aggregation based time delay neural network (ECAPA-TDNN)
architecture. We have conducted extensive experiments with three widely used
corpora for Indian LID research. In addition, we conduct a final blind
evaluation of our proposed methods on the Indian subset of VoxLingua107 corpus
collected in the wild. Our experiments demonstrate that the proposed domain
diversification is more promising over commonly used simple augmentation
methods. The study also reveals that domain generalization is a more effective
solution than domain diversification. We also notice that domain-aware learning
performs better for same-corpora LID, whereas domain-invariant learning is more
suitable for cross-corpora generalization. Compared to basic ECAPA-TDNN, its
proposed domain-invariant extensions improve the cross-corpora EER up to 5.23%.
In contrast, the proposed domain-aware extensions also improve performance for
same-corpora test scenarios.

研究低资源口语识别 (LID) 问题中跨领域泛化问题，提出领域多样性增强方法 (domain diversification)，并将其作为伪领域考虑，为此研究了领域不变和领域感知方法以及相关增强因子配置，提高了识别准确性。

领域多样化和泛化的跨语料库口语语言识别

Cross-Corpora Spoken Language Identification with Domain Diversification and Generalization

It is known that, without awareness of the process, our brain appears to
focus on the general shape of objects rather than superficial statistics of
context. On the other hand, learning autonomously allows discovering invariant
regularities which help generalization. In this work, we propose a learning
framework to improve the shape bias property of self-supervised methods. Our
method learns semantic and shape biased representations by integrating domain
diversification and jigsaw puzzles. The first module enables the model to
create a dynamic environment across arbitrary domains and provides a domain
exploration vs. exploitation trade-off, while the second module allows the
model to explore this environment autonomously. This universal framework does
not require prior knowledge of the domain of interest. Extensive experiments
are conducted on several domain generalization datasets, namely, PACS,
Office-Home, VLCS, and Digits. We show that our framework outperforms
state-of-the-art domain generalization methods by a large margin.

本研究提出了一种自我监督学习框架，通过整合领域多样性和拼图等方式，学习语义和形状倾向的表示，可用于领域泛化。实验结果表明，该框架明显优于现有的领域泛化方法。

面向形状偏向的领域通用无监督表示学习

Towards Shape Biased Unsupervised Representation Learning for Domain  Generalization

We introduce a novel unsupervised domain adaptation approach for object
detection. We aim to alleviate the imperfect translation problem of pixel-level
adaptations, and the source-biased discriminativity problem of feature-level
adaptations simultaneously. Our approach is composed of two stages, i.e.,
Domain Diversification (DD) and Multi-domain-invariant Representation Learning
(MRL). At the DD stage, we diversify the distribution of the labeled data by
generating various distinctive shifted domains from the source domain. At the
MRL stage, we apply adversarial learning with a multi-domain discriminator to
encourage feature to be indistinguishable among the domains. DD addresses the
source-biased discriminativity, while MRL mitigates the imperfect image
translation. We construct a structured domain adaptation framework for our
learning paradigm and introduce a practical way of DD for implementation. Our
method outperforms the state-of-the-art methods by a large margin of 3%~11% in
terms of mean average precision (mAP) on various datasets.

该研究介绍了一种针对目标检测的新型无监督域适应方法，通过域多样性和多域不变表示学习两个阶段来缓解像素级适应不完美和特征级适应带来的源偏差歧视。该方法在各种数据集上 mAP 平均精度方面的表现优于现有技术达到了 3%~11% 的大幅度提升。