Crowd counting has achieved significant progress by training regressors to
predict instance positions. In heavily crowded scenarios, however, regressors
are challenged by uncontrollable annotation variance, which causes density map
bias and context information inaccuracy. In this study, we propose mutual
prompt learning (mPrompt), which leverages a regressor and a segmenter as
guidance for each other, solving bias and inaccuracy caused by annotation
variance while distinguishing foreground from background. In specific, mPrompt
leverages point annotations to tune the segmenter and predict pseudo head masks
in a way of point prompt learning. It then uses the predicted segmentation
masks, which serve as spatial constraint, to rectify biased point annotations
as context prompt learning. mPrompt defines a way of mutual information
maximization from prompt learning, mitigating the impact of annotation variance
while improving model accuracy. Experiments show that mPrompt significantly
reduces the Mean Average Error (MAE), demonstrating the potential to be general
framework for down-stream vision tasks.

通过相互提示学习，减少注释变异对模型精确度的影响，提高人群计数的准确性。

回归器 - 分割器相互提示学习用于人群计数

Regressor-Segmenter Mutual Prompt Learning for Crowd Counting

Object tracking based on the fusion of visible and thermal im-ages, known as
RGB-T tracking, has gained increasing atten-tion from researchers in recent
years. How to achieve a more comprehensive fusion of information from the two
modalities with fewer computational costs has been a problem that re-searchers
have been exploring. Recently, with the rise of prompt learning in computer
vision, we can better transfer knowledge from visual large models to downstream
tasks. Considering the strong complementarity between visible and thermal
modalities, we propose a tracking architecture based on mutual prompt learning
between the two modalities. We also design a lightweight prompter that
incorporates attention mechanisms in two dimensions to transfer information
from one modality to the other with lower computational costs, embedding it
into each layer of the backbone. Extensive ex-periments have demonstrated that
our proposed tracking ar-chitecture is effective and efficient, achieving
state-of-the-art performance while maintaining high running speeds.

基于可见光和热红外图像融合的目标跟踪，称为 RGB-T 跟踪，在计算成本较低的情况下如何实现两种模态信息更全面的融合一直是研究人员探索的问题。最近，随着计算机视觉中即时学习的兴起，我们可以更好地将知识从视觉大模型转移到下游任务中。考虑到可见光和热红外模态之间的强互补性，我们提出了一种基于两种模态之间相互即时学习的跟踪架构。我们还设计了一个轻量级的即时学习器，在骨干网络的每一层中嵌入了二维注意机制，以低计算成本将信息从一种模态转移到另一种模态中。广泛的实验证明，我们提出的跟踪架构既有效又高效，在保持高运行速度的同时实现了最先进的性能。