CNN-LSTM cross-attention architecture combines convolutional feature extraction with sequential modeling and learned attention mechanisms for image understanding tasks. A 224x224x3 input image flows through three CNN layers (64, 128, 256 filters with batch normalization and max pooling), flattened into a sequence, then processed by three stacked LSTM layers (256 units each) that learn temporal dependencies. The cross-attention module fuses CNN-extracted spatial features as keys and values with LSTM-generated queries, enabling the model to dynamically focus on relevant image regions during sequence processing. This hybrid approach excels at tasks requiring both spatial pattern recognition and temporal context, such as video action recognition, image captioning, or visual question answering. Fork and customize this diagram on Diagrams.so to adapt layer depths, filter counts, or attention mechanisms for your specific dataset and inference requirements.