Large-scale pre-training followed by downstream fine-tuning is an effective
solution for transferring deep-learning-based models. Since finetuning all
possible pre-trained models is computational costly, we aim to predict the
transferability performance of these pre-trained models in a computational
efficient manner. Different from previous work that seek out suitable models
for downstream classification and segmentation tasks, this paper studies the
efficient transferability assessment of pre-trained object detectors. To this
end, we build up a detector transferability benchmark which contains a large
and diverse zoo of pre-trained detectors with various architectures, source
datasets and training schemes. Given this zoo, we adopt 7 target datasets from
5 diverse domains as the downstream target tasks for evaluation. Further, we
propose to assess classification and regression sub-tasks simultaneously in a
unified framework. Additionally, we design a complementary metric for
evaluating tasks with varying objects. Experimental results demonstrate that
our method outperforms other state-of-the-art approaches in assessing
transferability under different target domains while efficiently reducing
wall-clock time 32$\times$ and requires a mere 5.2\% memory footprint compared
to brute-force fine-tuning of all pre-trained detectors.

本研究通过建立一个包含各种结构、数据集和训练方法的大型目标检测器转移性基准，提出了一种高效的计算方式，同时评估分类和回归子任务，以及评估具有不同物体的任务。实验证明，相比于蛮力调整所有预训练检测器，我们的方法在不同目标领域下评估转移性能优于其他现有的方法，同时降低了 32 倍的时间和仅需 5.2% 的内存占用。

预训练检测器的高效可迁移性评估

Efficient Transferability Assessment for Selection of Pre-trained  Detectors

Unsupervised domain adaptive object detection aims to adapt a well-trained
detector from its original source domain with rich labeled data to a new target
domain with unlabeled data. Recently, mainstream approaches perform this task
through adversarial learning, yet still suffer from two limitations. First,
they mainly align marginal distribution by unsupervised cross-domain feature
matching, and ignore each feature's categorical and positional information that
can be exploited for conditional alignment; Second, they treat all classes as
equally important for transferring cross-domain knowledge and ignore that
different classes usually have different transferability. In this paper, we
propose a joint adaptive detection framework (JADF) to address the above
challenges. First, an end-to-end joint adversarial adaptation framework for
object detection is proposed, which aligns both marginal and conditional
distributions between domains without introducing any extra hyperparameter.
Next, to consider the transferability of each object class, a metric for
class-wise transferability assessment is proposed, which is incorporated into
the JADF objective for domain adaptation. Further, an extended study from
unsupervised domain adaptation (UDA) to unsupervised few-shot domain adaptation
(UFDA) is conducted, where only a few unlabeled training images are available
in unlabeled target domain. Extensive experiments validate that JADF is
effective in both the UDA and UFDA settings, achieving significant performance
gains over existing state-of-the-art cross-domain detection methods.

本文提出了一个联合自适应检测框架 (JADF)，通过联合、条件式对齐特征空间和返回类别空间，以提高无监督域自适应目标检测的性能，并提出了类别可迁移度评估指标来考虑每个对象类别的可迁移度，实现了无监督域自适应目标检测中 - state-of-the-art 性能。