用于重排目标检测的共尺度交叉注意力 Transformer
Co-Scale Cross-Attentional Transformer for Rearrangement Target Detection
AI总结：
针对重排目标检测中复杂形状与开合角度变化难以分割的问题，提出共尺度交叉注意力 Transformer，并构建 RGB-变化掩码数据集，在 F1 和 mIoU 上优于基线。
摘要：
将物体（例如花瓶、门）重新摆放回其原始位置，是家用服务机器人（DSR）最基本的技能之一。在重排任务中，关键在于根据目标状态和当前状态检测需要重新摆放的物体。本研究聚焦于重排目标检测（RTD），即模型为应当被重新摆放的物体生成变化掩码。尽管场景变化检测（SCD）领域已经开展了许多研究，但大多数 SCD 方法往往无法分割具有复杂形状的物体，也无法检测可打开或关闭物体的角度变化。本研究提出了一种用于 RTD 的共尺度交叉注意力 Transformer。我们引入了由一系列串行模块组成的 Serial Encoder（串行编码器），以及用于建模目标状态与当前状态之间关系的 Cross-Attentional Encoder（交叉注意力编码器）。我们构建了一个新数据集，其中包含与目标状态和当前状态相关的 RGB 图像及变化掩码。我们在该数据集上验证了所提方法，结果表明，本方法在 F1 分数和平均 IoU 上均优于基线方法。
英文摘要：
Rearranging objects (e.g. vase, door) back in their original positions is one of the most fundamental skills for domestic service robots (DSRs). In rearrangement tasks, it is crucial to detect the objects that need to be rearranged according to the goal and current states. In this study, we focus on Rearrangement Target Detection (RTD), where the model generates a change mask for objects that should be rearranged. Although many studies have been conducted in the field of Scene Change Detection (SCD), most SCD methods often fail to segment objects with complex shapes and fail to detect the change in the angle of objects that can be opened or closed. In this study, we propose a Co-Scale Cross-Attentional Transformer for RTD. We introduce the Serial Encoder which consists of a sequence of serial blocks and the Cross-Attentional Encoder which models the relationship between the goal and current states. We built a new dataset consisting of RGB images and change masks regarding the goal and current states. We validated our method on the dataset and the results demonstrated that our method outperformed baseline methods on $F_1$-score and mean IoU.
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。
