Suspected Object Matters: Rethinking Model's Prediction for One-stage Visual Grounding
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Recently, one-stage visual grounders attract high attention due to the comparable accuracy but significantly higher efficiency than two-stage grounders. However, inter-object relation modeling has not been well studied for one-stage grounders. Inter-object relationship modeling, though important, is not necessarily performed among all the objects within the image, as only a part of them are related to the text query and may confuse the model. We call these objects "suspected objects". However, exploring relationships among these suspected objects in the one-stage visual grounding paradigm is non-trivial due to two core problems: (1) no object proposals are available as the basis on which to select suspected objects and perform relationship modeling; (2) compared with those irrelevant to the text query, suspected objects are more confusing, as they may share similar semantics, be entangled with certain relationships, etc, and thereby more easily mislead the model's prediction. To address the above issues, this paper proposes a Suspected Object Graph (SOG) approach to encourage the correct referred object selection among the suspected ones in the one-stage visual grounding. Suspected objects are dynamically selected from a learned activation map as nodes to adapt to the current discrimination ability of the model during training. Afterward, on top of the suspected objects, a Keyword-aware Node Representation module (KNR) and an Exploration by Random Connection strategy (ERC) are concurrently proposed within the SOG to help the model rethink its initial prediction. Extensive ablation studies and comparison with state-of-the-art approaches on prevalent visual grounding benchmarks demonstrate the effectiveness of our proposed method.
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