SeCG：基于跨模态图注意力的语义增强三维视觉定位
SeCG: Semantic-Enhanced 3D Visual Grounding via Cross-modal Graph Attention
AI总结：
针对三维视觉定位中相似对象区分难及多指代关系理解不足的问题，提出基于跨模态图注意力的语义增强模型SeCG，在ReferIt3D和ScanRefer上超越现有方法。
摘要：
三维视觉定位旨在根据给定的文本描述自动定位指定对象的三维区域。现有方法难以区分相似对象，尤其是在描述涉及多个被指代对象时。实验表明，语言与视觉模态的直接匹配在理解话语中复杂的指代关系方面能力有限，这主要是由于跨模态对齐中冗余视觉信息造成的干扰。为了加强不同模态之间面向关系的映射，我们提出了SeCG，一种基于图网络的语义增强关系学习模型，并设计了记忆图注意力层。我们的方法在视觉分析中用跨模态编码取代了原有的语言无关编码。通过全局语义和隐含关系的引导，获得了更多与文本相关的特征表达。在ReferIt3D和ScanRefer基准上的实验结果表明，所提方法优于现有最先进方法，特别是在多关系挑战中显著提升了定位性能。
英文摘要：
3D visual grounding aims to automatically locate the 3D region of the specified object given the corresponding textual description. Existing works fail to distinguish similar objects especially when multiple referred objects are involved in the description. Experiments show that direct matching of language and visual modal has limited capacity to comprehend complex referential relationships in utterances. It is mainly due to the interference caused by redundant visual information in cross-modal alignment. To strengthen relation-orientated mapping between different modalities, we propose SeCG, a semantic-enhanced relational learning model based on a graph network with our designed memory graph attention layer. Our method replaces original language-independent encoding with cross-modal encoding in visual analysis. More text-related feature expressions are obtained through the guidance of global semantics and implicit relationships. Experimental results on ReferIt3D and ScanRefer benchmarks show that the proposed method outperforms the existing state-of-the-art methods, particularly improving the localization performance for the multi-relation challenges.
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。
