Event cameras are increasingly popular in robotics due to their beneficial
features, such as low latency, energy efficiency, and high dynamic range.
Nevertheless, their downstream task performance is greatly influenced by the
optimization of bias parameters. These parameters, for instance, regulate the
necessary change in light intensity to trigger an event, which in turn depends
on factors such as the environment lighting and camera motion. This paper
introduces feedback control algorithms that automatically tune the bias
parameters through two interacting methods: 1) An immediate, on-the-fly fast
adaptation of the refractory period, which sets the minimum interval between
consecutive events, and 2) if the event rate exceeds the specified bounds even
after changing the refractory period repeatedly, the controller adapts the
pixel bandwidth and event thresholds, which stabilizes after a short period of
noise events across all pixels (slow adaptation). Our evaluation focuses on the
visual place recognition task, where incoming query images are compared to a
given reference database. We conducted comprehensive evaluations of our
algorithms' adaptive feedback control in real-time. To do so, we collected the
QCR-Fast-and-Slow dataset that contains DAVIS346 event camera streams from 366
repeated traversals of a Scout Mini robot navigating through a 100 meter long
indoor lab setting (totaling over 35km distance traveled) in varying brightness
conditions with ground truth location information. Our proposed feedback
controllers result in superior performance when compared to the standard bias
settings and prior feedback control methods. Our findings also detail the
impact of bias adjustments on task performance and feature ablation studies on
the fast and slow adaptation mechanisms.

通过反馈控制算法自动调整偏置参数，该论文提出了适用于事件相机的快速和慢速自适应机制，来提高任务性能，尤其在可视地点识别任务中表现优异。

通过事件相机的快速和慢速自适应偏置增强视觉位置识别

Enhancing Visual Place Recognition via Fast and Slow Adaptive Biasing in  Event Cameras

Prediction head is a crucial component of Transformer language models.
Despite its direct impact on prediction, this component has often been
overlooked in analyzing Transformers. In this study, we investigate the inner
workings of the prediction head, specifically focusing on bias parameters. Our
experiments with BERT and GPT-2 models reveal that the biases in their word
prediction heads play a significant role in the models' ability to reflect word
frequency in a corpus, aligning with the logit adjustment method commonly used
in long-tailed learning. We also quantify the effect of controlling the biases
in practical auto-regressive text generation scenarios; under a particular
setting, more diverse text can be generated without compromising text quality.

本研究调查了预测头的内部工作，特别关注偏差参数，并发现 BERT 和 GPT-2 模型中的预测头偏差对模型反映语料库中的词频有重要作用，可通过对偏差的控制在实践自回归文本生成场景下生成更多样化的文本。