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Hyperparameter Analysis for Image Captioning

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arxiv 2006.10923 v1 pith:SHS4T6NI submitted 2020-06-19 cs.CV cs.LG

Amish Patel , Aravind Varier This is my paper · ORCID
classification cs.CVcs.LG
keywords experimentsanalysisarchitecturescaptioningcarriedimageapproachesbaseline
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In this paper, we perform a thorough sensitivity analysis on state-of-the-art image captioning approaches using two different architectures: CNN+LSTM and CNN+Transformer. Experiments were carried out using the Flickr8k dataset. The biggest takeaway from the experiments is that fine-tuning the CNN encoder outperforms the baseline and all other experiments carried out for both architectures.

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  1. An Ensemble Model with Attention Based Mechanism for Image Captioning

    cs.CV 2025-01 reject novelty 2.0 of 10

    An ensemble of eight CNN feature extractors plus a transformer reports high BLEU scores on Flickr8k and Flickr30k, but the final caption is selected using the test references, invalidating the evaluation.

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