Semantic Change Detection (SCD) is recognized as both a crucial and
challenging task in the field of image analysis. Traditional methods for SCD
have predominantly relied on the comparison of image pairs. However, this
approach is significantly hindered by substantial imaging differences, which
arise due to variations in shooting times, atmospheric conditions, and angles.
Such discrepancies lead to two primary issues: the under-detection of minor yet
significant changes, and the generation of false alarms due to temporal
variances. These factors often result in unchanged objects appearing markedly
different in multi-temporal images. In response to these challenges, the
MapChange framework has been developed. This framework introduces a novel
paradigm that synergizes temporal-invariant historical map data with
contemporary high-resolution images. By employing this combination, the
temporal variance inherent in conventional image pair comparisons is
effectively mitigated. The efficacy of the MapChange framework has been
empirically validated through comprehensive testing on two public datasets.
These tests have demonstrated the framework's marked superiority over existing
state-of-the-art SCD methods.

Semantic Change Detection（SCD）是图像分析领域中被认为是至关重要且具有挑战性的任务。传统 SCD 方法主要依赖于图像对比，但这种方法受到拍摄时间、大气条件和角度变化等的显著影响。为了解决这些问题，开发了 MapChange 框架，它将历史地图数据与高分辨率图像相结合，有效地减轻了传统图像对比中的时间变化影响。经过全面测试，该框架在两个公共数据集上表现出显著优势，验证了其有效性。

MapChange: 基于深度三元组网络的历史地图不变性增强语义变化检测

MapChange: Enhancing Semantic Change Detection with Temporal-Invariant  Historical Maps Based on Deep Triplet Network

Rewards play an essential role in reinforcement learning. In contrast to
rule-based game environments with well-defined reward functions, complex
real-world robotic applications, such as contact-rich manipulation, lack
explicit and informative descriptions that can directly be used as a reward.
Previous effort has shown that it is possible to algorithmically extract dense
rewards directly from multimodal observations. In this paper, we aim to extend
this effort by proposing a more efficient and robust way of sampling and
learning. In particular, our sampling approach utilizes temporal variance to
simulate the fluctuating state and action distribution of a manipulation task.
We then proposed a network architecture for self-supervised learning to better
incorporate temporal information in latent representations. We tested our
approach in two experimental setups, namely joint-assembly and door-opening.
Preliminary results show that our approach is effective and efficient in
learning dense rewards, and the learned rewards lead to faster convergence than
baselines.

本文提出了一种从多模态观察中抽取稠密奖励的更高效和更强韧的方法，在联合装配和开门两个实验设置中测试表明，这种方法在学习稠密奖励方面是有效和高效的，并且学习到的奖励导致更快的收敛。