improve weakly supervised visual grounding by learning where to focus on
Keywords: weakly supervised learning, visual grounding, grad-cam, vision and language
Abstract: Visual grounding is a crucial task for connecting visual and language descriptions by identifying target objects based on language entities. However, fully supervised methods require extensive annotations, which can be challenging and time-consuming to obtain. Weakly supervised visual grounding, which only relies on image-sentence association without object-level annotations, offers a promising solution. Previous approaches have mainly focused on finding the relationship between detected candidates, without considering improving object localization. In this work, we propose a novel method that leverages Grad-CAM to help the model identify precise objects. Specifically, we introduce a CAM encoder that exploits Grad-CAM information and a new loss function, attention mining loss, to guide the Grad-CAM feature to focus on the entire object. We also use an architecture which combines CNN and transformer, and a multi-modality fusion module to aggregate visual features, language features and CAM features. Our proposed approach achieves state-of-the-art results on several datasets, demonstrating its effectiveness in different scenes. Ablation studies further confirm the benefits of our architecture.
Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
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Submission Number: 12232
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