Sequence-to-Sequence

A neural network architecture designed for tasks where both input and output are sequences of variable length, such as machine translation, speech recognition, and video captioning. A seq2seq model consists of an encoder that processes the input sequence into a fixed-length representation (context vector), and a decoder that generates the output sequence from this representation. In accessibility applications, seq2seq models are used for sign language translation (video frames to text), automatic captioning, and speech-to-text conversion. While effective, basic seq2seq models struggle with long sequences; attention mechanisms and transformer architectures address this limitation.