Towards Real-Time Panoptic Narrative Grounding by an End-to-End Grounding Network

Haowei Wang; Jiayi Ji; Yiyi Zhou; Yongjian Wu; Xiaoshuai Sun

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arXiv · 2301.03160

Towards Real-Time Panoptic Narrative Grounding by an End-to-End Grounding Network

Haowei Wang·Jiayi Ji·Yiyi Zhou·Yongjian Wu·Xiaoshuai Sun

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Abstract

Panoptic Narrative Grounding (PNG) is an emerging cross-modal grounding task, which locates the target regions of an image corresponding to the text description. Existing approaches for PNG are mainly based on a two-stage paradigm, which is computationally expensive. In this paper, we propose a one-stage network for real-time PNG, termed End-to-End Panoptic Narrative Grounding network (EPNG), which directly generates masks for referents. Specifically, we propose two innovative designs, i.e., Locality-Perceptive Attention (LPA) and a bidirectional Semantic Alignment Loss (SAL), to properly handle the many-to-many relationship between textual expressions and visual objects. LPA embeds the local spatial priors into attention modeling, i.e., a pixel may belong to multiple masks at different scales, thereby improving segmentation. To help understand the complex semantic relationships, SAL proposes a bidirectional contrastive objective to regularize the semantic consistency inter modalities. Extensive experiments on the PNG benchmark dataset demonstrate the effectiveness and efficiency of our method. Compared to the single-stage baseline, our method achieves a significant improvement of up to 9.4% accuracy. More importantly, our EPNG is 10 times faster than the two-stage model. Meanwhile, the generalization ability of EPNG is also validated by zero-shot experiments on other grounding tasks.

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Haowei Wang, Jiayi Ji, Yiyi Zhou, Yongjian Wu, Xiaoshuai Sun. 2023-01-09. Towards Real-Time Panoptic Narrative Grounding by an End-to-End Grounding Network. https://arxiv.org/abs/2301.03160

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