Salient Object Detection with Dynamic Convolutions
Rohit Venkata Sai Dulam, Chandra Kambhamettu · 2025
Convolutional Neural Networks (CNNs) rely on contentindependent convolution operations that extract features shared across the entire dataset, limiting their adaptability to individual inputs. In contrast, input-dependent architectures like Vision Transformers (ViTs) can adapt to the specific characteristics of each input. To enhance input adaptability in CNNs, we propose SODDCNet, an encoderdecoder architecture for Salient Object Detection (SOD) that employs large convolutions with dynamically generated weights via the self-attention mechanism. Additionally, unlike other CNN architectures, we utilize multiple large kernels in parallel to segment salient objects of various sizes. To pre-train the proposed model, we combine the COCO and OpenImages semantic segmentation datasets to create a 3.18M image dataset for SOD. Comprehensive quantitative experiments conducted on benchmark datasets demonstrate that SODDCNet performs competitively compared to state-of-the-art methods in SOD and Video SOD. The code and pre-computed saliency maps are provided here.