IEEE Transactions on Circuits and Systems for Video Technology（2024）
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Abstract
Referring Expression Comprehension
(REC) is a fundamental task in the vision and language domain, which aims to locate an image region according to a natural language expression. REC requires the models to capture key clues in the text and perform accurate cross-modal reasoning. A recent trend employs transformer-based methods to address this problem. However, most of these methods typically treat image and text equally. They usually perform cross-modal reasoning in a crude way, and utilize textual features as a whole without detailed considerations (e.g., spatial information). This insufficient utilization of textual features will lead to sub-optimal results. In this paper, we propose a
Language Guided Reasoning Network
(LGR-NET) to fully utilize the guidance of the referring expression. To localize the referred object, we set a prediction token to capture cross-modal features. Furthermore, to sufficiently utilize the textual features, we extend them by our Textual Feature Extender (TFE) from three aspects.
First
, we design a novel coordinate embedding based on textual features. The coordinate embedding is incorporated to the prediction token to promote its capture of language-related visual features.
Second
, we employ the extracted textual features for Text-guided Cross-modal Alignment (TCA) and Fusion (TCF), alternately.
Third
, we devise a novel cross-modal loss to enhance cross-modal alignment between the referring expression and the learnable prediction token. We conduct extensive experiments on five benchmark datasets, and the experimental results show that our LGR-NET achieves a new state-of-the-art. Source code is available at https://github.com/lmc8133/LGR-NET.
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Key words
Vision and Language,Referring Expression Comprehension,Transformer,Cross-modal Reasoning
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