A Better Loss for Visual-Textual Grounding

• URL: http://arxiv.org/abs/2108.05308v1
• Date: Wed, 11 Aug 2021 16:26:54 GMT
• Title: A Better Loss for Visual-Textual Grounding
• Authors: Davide Rigoni, Luciano Serafini, Alessandro Sperduti
• Abstract summary: Given a textual phrase and an image, the visual grounding problem is defined as the task of locating the content of the image referenced by the sentence. It is a challenging task that has several real-world applications in human-computer interaction, image-text reference resolution, and video-text reference resolution. We propose a model that is able to achieve a higher accuracy than state-of-the-art models thanks to the adoption of a more effective loss function.
• Score: 74.81353762517979
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Given a textual phrase and an image, the visual grounding problem is defined as the task of locating the content of the image referenced by the sentence. It is a challenging task that has several real-world applications in human-computer interaction, image-text reference resolution, and video-text reference resolution. In the last years, several works have addressed this problem with heavy and complex models that try to capture visual-textual dependencies better than before. These models are typically constituted by two main components that focus on how to learn useful multi-modal features for grounding and how to improve the predicted bounding box of the visual mention, respectively. Finding the right learning balance between these two sub-tasks is not easy, and the current models are not necessarily optimal with respect to this issue. In this work, we propose a model that, although using a simple multi-modal feature fusion component, is able to achieve a higher accuracy than state-of-the-art models thanks to the adoption of a more effective loss function, based on the classes probabilities, that reach, in the considered datasets, a better learning balance between the two sub-tasks mentioned above.

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