Apr 27, 2020 · We propose a dense decoding module to make full use of the attention features of different scale ranges in the decoding process.

This work proposes an encoder-decoder framework with skip connections based on the self-attention mechanism, and applies the channel-spatial attention ...

Based on the self-attention mechanism, we apply the channel-spatial attention module as a transition layer, which captures the depth and spatial positional ...

Based on the self-attention mechanism, we apply the channel-spatial attention module as a transition layer, which captures the depth and spatial positional ...

Dec 12, 2024 · The proposal focuses on implementing a Convolutional Neural Network model with an attention mechanism configuration that has not yet been tested ...

This map provides information on three- dimensional scene geometry, which is necessary for various applications in academia and industry, such as robotics and.

Monocular depth estimation is the task of generating a dense predictive depth map from a single image. ... Attention-based context aggregation network for ...

In this paper, we propose a hybrid network with a Transformer-based encoder and a CNN-based decoder for monocular depth estimation. The encoder follows the ...

The dense neural network in the encoder significantly reduces information loss during propagation, promotes feature reuse, and enhances feature representation.

Dec 25, 2024 · This paper introduces a novel Transformer-based encoder-decoder network to enhance monocular depth estimation, presenting two key improvements ...