RSVG: Exploring Data and Models for Visual Grounding on Remote Sensing Data

• URL: http://arxiv.org/abs/2210.12634v1
• Date: Sun, 23 Oct 2022 07:08:22 GMT
• Title: RSVG: Exploring Data and Models for Visual Grounding on Remote Sensing Data
• Authors: Yang Zhan, Zhitong Xiong and Yuan Yuan
• Abstract summary: We introduce the task of visual grounding for remote sensing data (RSVG) RSVG aims to localize the referred objects in remote sensing (RS) images with the guidance of natural language. In this work, we construct a large-scale benchmark dataset of RSVG and explore deep learning models for the RSVG task.
• Score: 14.742224345061487
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we introduce the task of visual grounding for remote sensing data (RSVG). RSVG aims to localize the referred objects in remote sensing (RS) images with the guidance of natural language. To retrieve rich information from RS imagery using natural language, many research tasks, like RS image visual question answering, RS image captioning, and RS image-text retrieval have been investigated a lot. However, the object-level visual grounding on RS images is still under-explored. Thus, in this work, we propose to construct the dataset and explore deep learning models for the RSVG task. Specifically, our contributions can be summarized as follows. 1) We build the new large-scale benchmark dataset of RSVG, termed RSVGD, to fully advance the research of RSVG. This new dataset includes image/expression/box triplets for training and evaluating visual grounding models. 2) We benchmark extensive state-of-the-art (SOTA) natural image visual grounding methods on the constructed RSVGD dataset, and some insightful analyses are provided based on the results. 3) A novel transformer-based Multi-Level Cross-Modal feature learning (MLCM) module is proposed. Remotely-sensed images are usually with large scale variations and cluttered backgrounds. To deal with the scale-variation problem, the MLCM module takes advantage of multi-scale visual features and multi-granularity textual embeddings to learn more discriminative representations. To cope with the cluttered background problem, MLCM adaptively filters irrelevant noise and enhances salient features. In this way, our proposed model can incorporate more effective multi-level and multi-modal features to boost performance. Furthermore, this work also provides useful insights for developing better RSVG models. The dataset and code will be publicly available at https://github.com/ZhanYang-nwpu/RSVG-pytorch.

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