We hypothesize that end-to-end neural image captioning systems work seemingly well because they exploit and learn 'distributional similarity' in a multimodal ...
We hypothesize that end-to-end neural image captioning systems work seemingly well because they exploit and learn 'distributional similarity' in a multimodal ...
Abstract. We hypothesize that end-to-end neural image captioning systems work seemingly well because they exploit and learn 'distributional similarity' in a ...
It is concluded that, regardless of the image representation, image captioning systems seem to match images and generate captions in a learned joint ...
End-to-end image captioning models suffer no significant losses when the image representation is factorized to a low-dimensional space. There is scope to ...
Our model settings were: • LSTM with 128 dimensional word embeddings and 256 dimensional hidden represen- tations Dropout over LSTM of 0.8.
Captioning algorithms exploit multi-modal distributional similarity (Madhyastha et al., 2018) , and generate captions similar to images in the training set, ...
End-to-end Image Captioning Exploits Distributional Similarity in Multimodal Space. These are original images for Section 4.2: tsne_initial_boc_4000.png ...
In this paper, we propose a novel moving particle extraction method based on multimodal characteristic of the image. For the proposed particle extraction method ...
We hypothesize that end-to-end neural image captioning systems work seemingly well because they exploit and learn `distributional similarity' in a ...