Texture-Semantic Collaboration Network for ORSI Salient Object Detection

• URL: http://arxiv.org/abs/2312.03548v1
• Date: Wed, 6 Dec 2023 15:26:38 GMT
• Title: Texture-Semantic Collaboration Network for ORSI Salient Object Detection
• Authors: Gongyang Li, Zhen Bai, Zhi Liu
• Abstract summary: We propose a concise yet effective Texture-Semantic Collaboration Network (TSCNet) to explore the collaboration of texture cues and semantic cues for ORSI-SOD. TSCNet is based on the generic encoder-decoder structure and includes a vital Texture-Semantic Collaboration Module (TSCM) Our TSCNet achieves competitive performance compared to 14 state-of-the-art methods.
• Score: 13.724588317778753
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Salient object detection (SOD) in optical remote sensing images (ORSIs) has become increasingly popular recently. Due to the characteristics of ORSIs, ORSI-SOD is full of challenges, such as multiple objects, small objects, low illuminations, and irregular shapes. To address these challenges, we propose a concise yet effective Texture-Semantic Collaboration Network (TSCNet) to explore the collaboration of texture cues and semantic cues for ORSI-SOD. Specifically, TSCNet is based on the generic encoder-decoder structure. In addition to the encoder and decoder, TSCNet includes a vital Texture-Semantic Collaboration Module (TSCM), which performs valuable feature modulation and interaction on basic features extracted from the encoder. The main idea of our TSCM is to make full use of the texture features at the lowest level and the semantic features at the highest level to achieve the expression enhancement of salient regions on features. In the TSCM, we first enhance the position of potential salient regions using semantic features. Then, we render and restore the object details using the texture features. Meanwhile, we also perceive regions of various scales, and construct interactions between different regions. Thanks to the perfect combination of TSCM and generic structure, our TSCNet can take care of both the position and details of salient objects, effectively handling various scenes. Extensive experiments on three datasets demonstrate that our TSCNet achieves competitive performance compared to 14 state-of-the-art methods. The code and results of our method are available at https://github.com/MathLee/TSCNet.

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