SODAWideNet++: Combining Attention and Convolutions for Salient Object Detection
<|reference_start|>SODAWideNet++: Combining Attention and Convolutions for Salient Object Detection: Salient Object Detection (SOD) has traditionally relied on feature refinement modules that utilize the features of an ImageNet pre-trained backbone. However, this approach limits the possibility of pre-training the entire network because of the distinct nature of SOD and image classification. Additionally, the architecture of these backbones originally built for Image classification is sub-optimal for a dense prediction task like SOD. To address these issues, we propose a novel encoder-decoder-style neural network called SODAWideNet++ that is designed explicitly for SOD. Inspired by the vision transformers ability to attain a global receptive field from the initial stages, we introduce the Attention Guided Long Range Feature Extraction (AGLRFE) module, which combines large dilated convolutions and self-attention. Specifically, we use attention features to guide long-range information extracted by multiple dilated convolutions, thus taking advantage of the inductive biases of a convolution operation and the input dependency brought by self-attention. In contrast to the current paradigm of ImageNet pre-training, we modify 118K annotated images from the COCO semantic segmentation dataset by binarizing the annotations to pre-train the proposed model end-to-end. Further, we supervise the background predictions along with the foreground to push our model to generate accurate saliency predictions. SODAWideNet++ performs competitively on five different datasets while only containing 35% of the trainable parameters compared to the state-of-the-art models. The code and pre-computed saliency maps are provided at https://github.com/VimsLab/SODAWideNetPlusPlus.<|reference_end|>
@article{dulam2024sodawidenet++:,
    title={SODAWideNet++: Combining Attention and Convolutions for Salient Object
  Detection},
    author={Rohit Venkata Sai Dulam and Chandra Kambhamettu},
    journal={arXiv preprint arXiv:2408.16645},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.16645},
    primaryClass={cs.CV}
}