Entity-enhanced Adaptive Reconstruction Network for Weakly Supervised Referring Expression Grounding

作     者：Liu, Xuejing Li, Liang Wang, Shuhui Zha, Zheng-Jun Li, Zechao Tian, Qi Huang, Qingming 

作者机构： Institute of Computing Technology CAS Beijing100190 China University of Chinese Academy of Sciences Beijing100190 China Peng Cheng Laboratory Shenzhen518066 China The School of Information Science and Technology University of Science and Technology of China Hefei230027 China The School of Computer Science Nanjing University of Science and Technology Nanjing210094 China Huawei Cloud & AI Shenzhen518129 China The School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing100190 China The Institute of Computing Technology CAS Beijing100190 China 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2022年

核心收录：

主　　题：Semantics 

摘      要：Weakly supervised Referring Expression Grounding (REG) aims to ground a particular target in an image described by a language expression while lacking the correspondence between target and expression. Two main problems exist in weakly supervised REG. First, the lack of region-level annotations introduces ambiguities between proposals and queries. Second, most previous weakly supervised REG methods ignore the discriminative location and context of the referent, causing difficulties in distinguishing the target from other same-category objects. To address the above challenges, we design an entity-enhanced adaptive reconstruction network (EARN). Specifically, EARN includes three modules: entity enhancement, adaptive grounding, and collaborative reconstruction. In entity enhancement, we calculate semantic similarity as supervision to select the candidate proposals. Adaptive grounding calculates the ranking score of candidate proposals upon subject, location and context with hierarchical attention. Collaborative reconstruction measures the ranking result from three perspectives: adaptive reconstruction, language reconstruction and attribute classification. The adaptive mechanism helps to alleviate the variance of different referring expressions. Experiments on five datasets show EARN outperforms existing state-of-the-art methods. Qualitative results demonstrate that the proposed EARN can better handle the situation where multiple objects of a particular category are situated together. Copyright © 2022, The Authors. All rights reserved.