| Author(s) | Lou, J., Lin, H., Marshall, D., Saupe, D., Liu, H. |
| Title | TranSalNet: Visual saliency prediction using transformers |
| Abstract | Convolutional neural networks (CNNs) have significantly advanced computational modeling for saliency prediction. However, the inherent inductive biases of convolutional architectures cause insufficient long-range contextual encoding capacity, which potentially makes a saliency model less humanlike. Transformers have shown great potential in encoding long-range information by leveraging the self-attention mechanism. In this paper, we propose a novel saliency model integrating transformer components to CNNs to capture the long-range contextual information. Experimental results show that the new components make improvements, and the proposed model achieves promising results in predicting saliency. |