--- title: "Proposal-free One-stage Referring Expression via Grid-Word Cross-Attention" authors: - "Wei Suo" - "MengYang Sun" - "Peng Wang" - "Qi Wu" doi: "10.24963/ijcai.2021/143" url: "https://ijcai.org/proceedings/2021/143" pdf: "https://ijcai.org/proceedings/2021/0143.pdf" venue: "Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence" year: 2021 pages: "1032-1038" track: "Main Track" keywords: - "Computer Vision: Language and Vision" - "Computer Vision: Structural and Model-Based Approaches, Knowledge Representation and Reasoning" --- # Proposal-free One-stage Referring Expression via Grid-Word Cross-Attention Wei Suo, MengYang Sun, Peng Wang, Qi Wu *Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence*, pages 1032-1038. DOI: [10.24963/ijcai.2021/143](https://doi.org/10.24963/ijcai.2021/143) ## Abstract Referring Expression Comprehension (REC) has become one of the most important tasks in visual reasoning, since it is an essential step for many vision-and-language tasks such as visual question answering. However, it has not been widely used in many downstream tasks because it suffers 1) two-stage methods exist heavy computation cost and inevitable error accumulation, and 2) one-stage methods have to depend on lots of hyper-parameters (such as anchors) to generate bounding box. In this paper, we present a proposal-free one-stage (PFOS) model that is able to regress the region-of-interest from the image, based on a textual query, in an end-to-end manner. Instead of using the dominant anchor proposal fashion, we directly take the dense-grid of image as input for a cross-attention transformer that learns grid-word correspondences. The final bounding box is predicted directly from the image without the time-consuming anchor selection process that previous methods suffer. Our model achieves the state-of-the-art performance on four referring expression datasets with higher efficiency, comparing to previous best one-stage and two-stage methods.