With a Little Help from your own Past: Prototypical Memory Networks for Image Captioning

• URL: http://arxiv.org/abs/2308.12383v1
• Date: Wed, 23 Aug 2023 18:53:00 GMT
• Title: With a Little Help from your own Past: Prototypical Memory Networks for Image Captioning
• Authors: Manuele Barraco, Sara Sarto, Marcella Cornia, Lorenzo Baraldi, Rita Cucchiara
• Abstract summary: We devise a network which can perform attention over activations obtained while processing other training samples. Our memory models the distribution of past keys and values through the definition of prototype vectors. We demonstrate that our proposal can increase the performance of an encoder-decoder Transformer by 3.7 CIDEr points both when training in cross-entropy only and when fine-tuning with self-critical sequence training.
• Score: 47.96387857237473
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image captioning, like many tasks involving vision and language, currently relies on Transformer-based architectures for extracting the semantics in an image and translating it into linguistically coherent descriptions. Although successful, the attention operator only considers a weighted summation of projections of the current input sample, therefore ignoring the relevant semantic information which can come from the joint observation of other samples. In this paper, we devise a network which can perform attention over activations obtained while processing other training samples, through a prototypical memory model. Our memory models the distribution of past keys and values through the definition of prototype vectors which are both discriminative and compact. Experimentally, we assess the performance of the proposed model on the COCO dataset, in comparison with carefully designed baselines and state-of-the-art approaches, and by investigating the role of each of the proposed components. We demonstrate that our proposal can increase the performance of an encoder-decoder Transformer by 3.7 CIDEr points both when training in cross-entropy only and when fine-tuning with self-critical sequence training. Source code and trained models are available at: https://github.com/aimagelab/PMA-Net.

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