Neuroscience studies have shown that the human visual system utilizes
high-level feedback information to guide lower-level perception, enabling
adaptation to signals of different characteristics. In light of this, we
propose Feedback multi-Level feature Extractor (Flex) to incorporate a similar
mechanism for object detection. Flex refines feature selection based on
image-wise and instance-level feedback information in response to image quality
variation and classification uncertainty. Experimental results show that Flex
offers consistent improvement to a range of existing SOTA methods on the
challenging aerial object detection datasets including DOTA-v1.0, DOTA-v1.5,
and HRSC2016. Although the design originates in aerial image detection, further
experiments on MS COCO also reveal our module's efficacy in general detection
models. Quantitative and qualitative analyses indicate that the improvements
are closely related to image qualities, which match our motivation.

人类的视觉系统利用高级反馈信息来指导低级知觉，以适应不同特征的信号。通过引入类似机制的反馈多级特征提取器（Flex）来改进目标检测。实验证明，Flex 在多个挑战性航空目标检测数据集（包括 DOTA-v1.0、DOTA-v1.5 和 HRSC2016）上对一系列现有的最先进方法均提供了一致的改进。虽然设计起源于航空图像检测，但进一步对 MS COCO 进行的实验证明了我们模块在一般检测模型中的有效性。定量和定性分析表明，这些改进与图像质量密切相关，与我们的动机相一致。

反馈 RoI 特征改善航空目标检测

Feedback RoI Features Improve Aerial Object Detection

A central challenge in human pose estimation, as well as in many other
machine learning and prediction tasks, is the generalization problem. The
learned network does not have the capability to characterize the prediction
error, generate feedback information from the test sample, and correct the
prediction error on the fly for each individual test sample, which results in
degraded performance in generalization. In this work, we introduce a
self-correctable and adaptable inference (SCAI) method to address the
generalization challenge of network prediction and use human pose estimation as
an example to demonstrate its effectiveness and performance. We learn a
correction network to correct the prediction result conditioned by a fitness
feedback error. This feedback error is generated by a learned fitness feedback
network which maps the prediction result to the original input domain and
compares it against the original input. Interestingly, we find that this
self-referential feedback error is highly correlated with the actual prediction
error. This strong correlation suggests that we can use this error as feedback
to guide the correction process. It can be also used as a loss function to
quickly adapt and optimize the correction network during the inference process.
Our extensive experimental results on human pose estimation demonstrate that
the proposed SCAI method is able to significantly improve the generalization
capability and performance of human pose estimation.

本研究提出了自修改和适应推理（SCAI）方法来解决网络预测的泛化挑战，并使用人体姿势估计作为示例来展示其有效性和性能。