Liuwu Li


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Segment-Level and Category-Oriented Network for Knowledge-Based Referring Expression Comprehension
Yuqi Bu | Xin Wu | Liuwu Li | Yi Cai | Qiong Liu | Qingbao Huang
Findings of the Association for Computational Linguistics: ACL 2023

Knowledge-based referring expression comprehension (KB-REC) aims to identify visual objects referred to by expressions that incorporate knowledge. Existing methods employ sentence-level retrieval and fusion methods, which may lead to issues of similarity bias and interference from irrelevant information in unstructured knowledge sentences. To address these limitations, we propose a segment-level and category-oriented network (SLCO). Our approach includes a segment-level and prompt-based knowledge retrieval method to mitigate the similarity bias problem and a category-based grounding method to alleviate interference from irrelevant information in knowledge sentences. Experimental results show that our SLCO can eliminate interference and improve the overall performance of the KB-REC task.

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Scene Graph Enhanced Pseudo-Labeling for Referring Expression Comprehension
Cantao Wu | Yi Cai | Liuwu Li | Jiexin Wang
Findings of the Association for Computational Linguistics: EMNLP 2023

Referring Expression Comprehension (ReC) is a task that involves localizing objects in images based on natural language expressions. Most ReC methods typically approach the task as a supervised learning problem. However, the need for costly annotations, such as clear image-text pairs or region-text pairs, hinders the scalability of existing approaches. In this work, we propose a novel scene graph-based framework that automatically generates high-quality pseudo region-query pairs. Our method harnesses scene graphs to capture the relationships between objects in images and generate expressions enriched with relation information. To ensure accurate mapping between visual regions and text, we introduce an external module that employs a calibration algorithm to filter out ambiguous queries. Additionally, we employ a rewriter module to enhance the diversity of our generated pseudo queries through rewriting. Extensive experiments demonstrate that our method outperforms previous pseudo-labeling methods by about 10%, 12%, and 11% on RefCOCO, RefCOCO+, and RefCOCOg, respectively. Furthermore, it surpasses the state-of-the-art unsupervised approach by more than 15% on the RefCOCO dataset.