GradAttn: Replacing Fixed Residual Connections with Task-Modulated Attention Pathways
Deep ConvNets suffer from gradient signal degradation as network depth increases, limiting effective feature learning in complex architectures. ResNet addressed this through residual connections, but these fixed short circuits cannot adapt to varying input complexity or selectively emphasize task-relevant features across network hierarchies. This study introduces GradAttn, a variation of the residual approach in CNNs that replaces the fixed residual connections with attention-controlled gradient flow. By extracting multi-scale CNN features at different depths and regulating them through self-attention, GradAttn dynamically weights shallow texture features and deep semantic representations...
| Authors | Soudeep Ghoshal, Himanshu Buckchash |
|---|---|
| Primary category | cs.CV |
| Published | 23 March 2026 |
| Last updated at source | 22 August 2026 |
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