探索面向RGB-D SLAM的物体感知注意力引导帧关联
Exploring Object-Aware Attention Guided Frame Association for RGB-D SLAM
AI总结：
本文提出将任务特定的网络梯度注意力与CNN特征结合，用于RGB-D室内SLAM的帧关联，有效提升了大型环境下的性能。
摘要：
注意力模型近期作为一种强大方法出现，在各个领域展现出显著进展。诸如类激活映射等可视化技术为卷积神经网络（CNN）的推理提供了视觉洞察。利用网络梯度，可以识别出网络在图像识别任务中关注的区域。此外，这些梯度可与CNN特征结合，以定位场景中更具泛化性、任务特定的注意（显著）区域。然而，将这种基于梯度的注意力信息直接整合到CNN表示中用于语义物体理解的做法仍然有限。这种整合对于同时定位与建图（SLAM）等视觉任务尤为有益，因为融入了空间注意物体位置的CNN表示能够提升性能。在本研究中，我们提出利用任务特定的网络注意力用于RGB-D室内SLAM。具体而言，我们将源自网络梯度的逐层注意力信息与CNN特征表示相结合，以提升帧关联性能。实验结果表明，相较于基线方法有性能提升，尤其是在大型环境中。
英文摘要：
Attention models have recently emerged as a powerful approach, demonstrating significant progress in various fields. Visualization techniques, such as class activation mapping, provide visual insights into the reasoning of convolutional neural networks (CNNs). Using network gradients, it is possible to identify regions where the network pays attention during image recognition tasks. Furthermore, these gradients can be combined with CNN features to localize more generalizable, task-specific attentive (salient) regions within scenes. However, explicit use of this gradient-based attention information integrated directly into CNN representations for semantic object understanding remains limited. Such integration is particularly beneficial for visual tasks like simultaneous localization and mapping (SLAM), where CNN representations enriched with spatially attentive object locations can enhance performance. In this work, we propose utilizing task-specific network attention for RGB-D indoor SLAM. Specifically, we integrate layer-wise attention information derived from network gradients with CNN feature representations to improve frame association performance. Experimental results indicate improved performance compared to baseline methods, particularly for large environments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
