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

We show that for human-object interaction detection a relatively simple
factorized model with appearance and layout encodings constructed from
pre-trained object detectors outperforms more sophisticated approaches. Our
model includes factors for detection scores, human and object appearance, and
coarse (box-pair configuration) and optionally fine-grained layout (human
pose). We also develop training techniques that improve learning efficiency by:
(1) eliminating a train-inference mismatch; (2) rejecting easy negatives during
mini-batch training; and (3) using a ratio of negatives to positives that is
two orders of magnitude larger than existing approaches. We conduct a thorough
ablation study to understand the importance of different factors and training
techniques using the challenging HICO-Det dataset.

本研究发现，在人物 - 物体交互检测方面，使用预训练的物体检测器构建的外观和布局编码的相对简单的分解模型优于更复杂的方法。同时，作者还开发了一些训练技巧来提高学习效率。研究使用现有的 HICO-Det 数据集展开了深入的剖析，以了解不同因素和培训技术的重要性。