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arXiv · 2309.17205

Towards Complex-query Referring Image Segmentation: A Novel Benchmark

Wei Ji·Li Li·Hao Fei·Xiangyan Liu·Xun Yang·Juncheng Li·Roger Zimmermann
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Abstract

Referring Image Understanding (RIS) has been extensively studied over the past decade, leading to the development of advanced algorithms. However, there has been a lack of research investigating how existing algorithms should be benchmarked with complex language queries, which include more informative descriptions of surrounding objects and backgrounds (\eg \textit{"the black car."} vs. \textit{"the black car is parking on the road and beside the bus."}). Given the significant improvement in the semantic understanding capability of large pre-trained models, it is crucial to take a step further in RIS by incorporating complex language that resembles real-world applications. To close this gap, building upon the existing RefCOCO and Visual Genome datasets, we propose a new RIS benchmark with complex queries, namely \textbf{RIS-CQ}. The RIS-CQ dataset is of high quality and large scale, which challenges the existing RIS with enriched, specific and informative queries, and enables a more realistic scenario of RIS research. Besides, we present a nichetargeting method to better task the RIS-CQ, called dual-modality graph alignment model (\textbf{\textsc{DuMoGa}}), which outperforms a series of RIS methods.

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Wei Ji, Li Li, Hao Fei, Xiangyan Liu, Xun Yang, Juncheng Li, Roger Zimmermann. 2023-09-29. Towards Complex-query Referring Image Segmentation: A Novel Benchmark. https://arxiv.org/abs/2309.17205

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