PGNeXt: High-Resolution Salient Object Detection via Pyramid Grafting Network
<|reference_start|>PGNeXt: High-Resolution Salient Object Detection via Pyramid Grafting Network: We present an advanced study on more challenging high-resolution salient object detection (HRSOD) from both dataset and network framework perspectives. To compensate for the lack of HRSOD dataset, we thoughtfully collect a large-scale high resolution salient object detection dataset, called UHRSD, containing 5,920 images from real-world complex scenarios at 4K-8K resolutions. All the images are finely annotated in pixel-level, far exceeding previous low-resolution SOD datasets. Aiming at overcoming the contradiction between the sampling depth and the receptive field size in the past methods, we propose a novel one-stage framework for HR-SOD task using pyramid grafting mechanism. In general, transformer-based and CNN-based backbones are adopted to extract features from different resolution images independently and then these features are grafted from transformer branch to CNN branch. An attention-based Cross-Model Grafting Module (CMGM) is proposed to enable CNN branch to combine broken detailed information more holistically, guided by different source feature during decoding process. Moreover, we design an Attention Guided Loss (AGL) to explicitly supervise the attention matrix generated by CMGM to help the network better interact with the attention from different branches. Comprehensive experiments on UHRSD and widely-used SOD datasets demonstrate that our method can simultaneously locate salient object and preserve rich details, outperforming state-of-the-art methods. To verify the generalization ability of the proposed framework, we apply it to the camouflaged object detection (COD) task. Notably, our method performs superior to most state-of-the-art COD methods without bells and whistles.<|reference_end|>
@article{xia2024pgnext:,
    title={PGNeXt: High-Resolution Salient Object Detection via Pyramid Grafting
  Network},
    author={Changqun Xia, Chenxi Xie, Zhentao He, Tianshu Yu, and Jia Li},
    journal={arXiv preprint arXiv:2408.01137},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.01137},
    primaryClass={cs.CV}
}