# Neural Twins Talk & Alternative Calculations

arXiv: https://arxiv.org/abs/2108.02807 · HTML: https://mlsanity.com/paper/2108.02807/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 26 of its 41 references that are in the map.
- First posted on arXiv 5 Aug 2021.

## Builds on (most cited first, 26 of 26)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [Show, Attend and Tell: Neural Image Caption Generation with Visual Attention](https://mlsanity.com/paper/1502.03044/) (2015)
- [Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering](https://mlsanity.com/paper/1707.07998/) (2017)
- [Deep Visual-Semantic Alignments for Generating Image Descriptions](https://mlsanity.com/paper/1412.2306/) (2014)
- [Pointer Networks](https://mlsanity.com/paper/1506.03134/) (2015)
- [Show and Tell: A Neural Image Caption Generator](https://mlsanity.com/paper/1411.4555/) (2014)
- [SPICE: Semantic Propositional Image Caption Evaluation](https://mlsanity.com/paper/1607.08822/) (2016)
- [Long-term Recurrent Convolutional Networks for Visual Recognition and Description](https://mlsanity.com/paper/1411.4389/) (2014)
- [Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning](https://mlsanity.com/paper/1612.01887/) (2016)
- [A Comprehensive Survey of Deep Learning for Image Captioning](https://mlsanity.com/paper/1810.04020/) (2018)
- [DenseCap: Fully Convolutional Localization Networks for Dense Captioning](https://mlsanity.com/paper/1511.07571/) (2015)
- [Neural Baby Talk](https://mlsanity.com/paper/1803.09845/) (2018)
- [Deep Fragment Embeddings for Bidirectional Image Sentence Mapping](https://mlsanity.com/paper/1406.5679/) (2014)
- [Ask, Attend and Answer: Exploring Question-Guided Spatial Attention for Visual Question Answering](https://mlsanity.com/paper/1511.05234/) (2015)
- [Deep Compositional Captioning: Describing Novel Object Categories without Paired Training Data](https://mlsanity.com/paper/1511.05284/) (2015)
- [Describing Multimedia Content using Attention-based Encoder--Decoder Networks](https://mlsanity.com/paper/1507.01053/) (2015)
- [Automatic Concept Discovery from Parallel Text and Visual Corpora](https://mlsanity.com/paper/1509.07225/) (2015)

## Abstract (arXiv)

Inspired by how the human brain employs a higher number of neural pathways when describing a highly focused subject, we show that deep attentive models used for the main vision-language task of image captioning, could be extended to achieve better performance. Image captioning bridges a gap between computer vision and natural language processing. Automated image captioning is used as a tool to eliminate the need for human agent for creating descriptive captions for unseen images.Automated image captioning is challenging and yet interesting. One reason is that AI based systems capable of generating sentences that describe an input image could be used in a wide variety of tasks beyond generating captions for unseen images found on web or uploaded to social media. For example, in biology and medical sciences, these systems could provide researchers and physicians with a brief linguistic description of relevant images, potentially expediting their work.