Top-Down Framework for Weakly-supervised Grounded Image Captioning

• URL: http://arxiv.org/abs/2306.07490v3
• Date: Sat, 2 Mar 2024 15:10:16 GMT
• Title: Top-Down Framework for Weakly-supervised Grounded Image Captioning
• Authors: Chen Cai, Suchen Wang, Kim-hui Yap, Yi Wang
• Abstract summary: Weakly-supervised grounded image captioning aims to generate the caption and ground (localize) predicted object words in the input image without using bounding box supervision. We propose a one-stage weakly-supervised grounded captioner that directly takes the RGB image as input to perform captioning and grounding at the top-down image level.
• Score: 19.00510117145054
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Weakly-supervised grounded image captioning (WSGIC) aims to generate the caption and ground (localize) predicted object words in the input image without using bounding box supervision. Recent two-stage solutions mostly apply a bottom-up pipeline: (1) encode the input image into multiple region features using an object detector; (2) leverage region features for captioning and grounding. However, utilizing independent proposals produced by object detectors tends to make the subsequent grounded captioner overfitted in finding the correct object words, overlooking the relation between objects, and selecting incompatible proposal regions for grounding. To address these issues, we propose a one-stage weakly-supervised grounded captioner that directly takes the RGB image as input to perform captioning and grounding at the top-down image level. Specifically, we encode the image into visual token representations and propose a Recurrent Grounding Module (RGM) in the decoder to obtain precise Visual Language Attention Maps (VLAMs), which recognize the spatial locations of the objects. In addition, we explicitly inject a relation module into our one-stage framework to encourage relation understanding through multi-label classification. This relation semantics served as contextual information facilitating the prediction of relation and object words in the caption. We observe that the relation semantic not only assists the grounded captioner in generating a more accurate caption but also improves the grounding performance. We validate the effectiveness of our proposed method on two challenging datasets (Flick30k Entities captioning and MSCOCO captioning). The experimental results demonstrate that our method achieves state-of-the-art grounding performance.

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