Jul 3, 2024 · CNN encodes the input image into a 1-D array, whereas RNN or LSTM generates the caption. Figure 1 shows a schematic block diagram of the encoder-decoder based image captioning model.

Jan 19, 2021 · Decoder Network. The decoder network takes the abstract representation generated by the encoder and generates a semantic segmentation mask. The decoder block starts with a 2×2 transpose convolution, which is then concatenated with the corresponding skip connection feature map from the encoder block.

Our uni-fied framework shows that encoder-decoder CNN architecture is closely related to nonlinear frame representation using combinatorial convolution frames, whose expressibility increases exponen-tially with the depth. We also demonstrate the importance of skipped connection in terms of ex-pressibility, and optimization landscape. 1.

Nov 25, 2024 · This simplified diagram illustrates the sequential processing steps involved in image caption generation, from extracting features with the CNN encoder to generating textual descriptions with the LSTM decoder.

FIGURE 1 A typical block diagram of deep learning-based encoder–decoder pipeline for automatic image captioning. and picture encoding processes were both enhanced by this approach.

Block diagram of an image captioning model using deep learning encoder-decoder framework. This study is concerned with the development of a deep neural network-based...

In the same direction, two different models based on the CNN and the encoder-decoder method were established to predict the characteristics of the flow velocity and heat transfer around the NACA ...

The encoder is composed of four Dense Blocks connected by Downsampling blocks. The decoder uses three Residual Blocks connected by transpose convolutions. Several skip connections transfer ...

Mar 24, 2025 · The Models is a combination of encoder and decoder wherein encoder is a CNN model responsible for the feature extraction and Decoder is a language model i.e. LSTM model responsible for generating the textual description. Figure 2 shows the block diagram for the proposed system.

Ac-cordingly, we have revealed the following geometric fea-tures of encoder-decoder CNNs: An encoder-decoder CNN with an over-parameterized feature layer approximates a map between two smooth manifolds that is decomposed as a high-dimensional embedding followed by a quotient map. Figure 1.