Apr 5, 2025 · SegNet is a deep learning architecture designed for semantic segmentation, where the goal is to classify each pixel in an image into a predefined category. It is an encoder-decoder neural network tailored for pixel-wise image segmentation, making it highly effective for tasks that require detailed and precise segmentation of images.

Aug 16, 2024 · The Encoder-Decoder architecture is a fundamental concept in machine learning, especially in tasks involving sequences such as machine translation, text summarization, and image captioning....

Mar 12, 2021 · In this article, I aim to explain the encoder-decoder sequence-to-sequence models in detail and help build your intuition behind its working. For this, I have taken a step-by-step...

Jun 24, 2023 · Using an encoder-decoder architecture, the model can take an input image and generate a caption that accurately describes the contents of the image. This is achieved by first encoding each...

May 27, 2015 · SegNet is composed of a stack of encoders followed by a corresponding decoder stack which feeds into a soft-max classification layer. The decoders help map low resolution feature maps at the output of the encoder stack to full input image size feature maps.

Develop an encoder-decoder model with CNN for vision and RNN/LSTM/Transformer for sequence. Optimize with hyperparameters, overfitting/underfitting techniques, and different optimizers. Evaluated on Flickr8k dataset using BLEU score metric.

Encoder-decoder architectures can handle inputs and outputs that both consist of variable-length sequences and thus are suitable for sequence-to-sequence problems such as machine translation. The encoder takes a variable-length sequence as input and transforms it into a state with a fixed shape.

Jan 2, 2017 · SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation Abstract: We present a novel and practical deep fully convolutional neural network architecture for semantic pixel-wise segmentation termed SegNet.

In this research work, EfficientNetV2B0 is utilized in the encoder part, for extracting objects from an image. Then Long Short-Term Memory (LSTM), a type of recurrent neural network as a decoder for generating descriptions by using encoder output.

This core trainable segmentation architecture consists of an encoder network, a corresponding decoder network followed by a pixel-wise classification layer. The architecture of the encoder network is topologically identical to the 13 convolutional layers in the VGG16 network.