Transformer-Based Attention Networks for Continuous Pixel-Wise Prediction

Understand

While convolutional neural networks have shown a tremendous impact on various computer vision tasks, they generally demonstrate limitations in explicitly modeling long-range dependencies due to the intrinsic locality of the convolution operation.

• Initially designed for natural language processing tasks, Transformers have emerged as alternative architectures with innate global self-attention mechanisms to capture long-range dependencies.
• In this paper, we propose TransDepth, an architecture that benefits from both convolutional neural networks and transformers.
• To avoid the network losing its ability to capture local-level details due to the adoption of transformers, we propose a novel decoder that employs attention mechanisms based on gates.