Question 1

How to use Show, Attend and Tell with my own images?

Accepted Answer

You need to preprocess images similarly to MSCOCO: resize to 224x224, extract VGGNet19 features, and modify the data pipeline. The project doesn't support custom datasets out-of-the-box, so code adjustments are required.

Question 2

Show, Attend and Tell vs. newer Transformer-based captioning models?

Accepted Answer

This model uses an LSTM with attention, which is foundational but may underperform compared to state-of-the-art Transformers. It's best for learning attention mechanisms rather than achieving top benchmark scores.

Question 3

How to visualize attention maps in Show, Attend and Tell?

Accepted Answer

Use the evaluate_model.ipynb notebook provided, which generates captions and plots attention weights over time, as demonstrated in the README's example images with soft attention overlays.

Question 4

What performance metrics does Show, Attend and Tell achieve on MSCOCO?

Accepted Answer

The README doesn't list exact BLEU or CIDEr scores; it shows qualitative results. For benchmarks, refer to the original paper or more recent implementations with updated evaluations.

Question 5

How to install Show, Attend and Tell with TensorFlow 2?

Accepted Answer

It's not directly compatible; you'll need to port the code to TensorFlow 2 by updating API calls and potentially retraining, which can be complex due to deprecated functions.

Question 6

Can Show, Attend and Tell generate captions in real-time?

Accepted Answer

No, it's not optimized for real-time use; the focus is on research and training, with preprocessing and model inference that add latency, making it unsuitable for live applications without modifications.

Show, Attend and Tell

What is Show, Attend and Tell?

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