Neural Twins Talk & Alternative Calculations
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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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 26 of its 41 references that are in the map. First posted on arXiv 5 Aug 2021.
Builds on
26 of 41 references.
References · 26
- 2017Attention Is All You Needcited by 59,932
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2014Adam: A Method for Stochastic Optimizationcited by 36,960
- 2014Microsoft COCO: Common Objects in Contextcited by 17,498
- 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
- 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
- 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,530
- 2014Sequence to Sequence Learning with Neural Networkscited by 2,974
- 2015Fast R-CNNcited by 2,930
- 2016Aggregated Residual Transformations for Deep Neural Networkscited by 2,540
- 2015Show, Attend and Tell: Neural Image Caption Generation with Visual Attentioncited by 1,421
- 2017Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answeringcited by 1,310
14 more
- 2014Deep Visual-Semantic Alignments for Generating Image Descriptionscited by 966
- 2015Pointer Networkscited by 882
- 2014Show and Tell: A Neural Image Caption Generatorcited by 877
- 2016SPICE: Semantic Propositional Image Caption Evaluationcited by 820
- 2014Long-term Recurrent Convolutional Networks for Visual Recognition and Descriptioncited by 483
- 2016Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioningcited by 231
- 2018A Comprehensive Survey of Deep Learning for Image Captioningcited by 213
- 2015DenseCap: Fully Convolutional Localization Networks for Dense Captioningcited by 176
- 2018Neural Baby Talkcited by 107
- 2014Deep Fragment Embeddings for Bidirectional Image Sentence Mappingcited by 99
- 2015Ask, Attend and Answer: Exploring Question-Guided Spatial Attention for Visual Question Answeringcited by 66
- 2015Deep Compositional Captioning: Describing Novel Object Categories without Paired Training Datacited by 36
- 2015Describing Multimedia Content using Attention-based Encoder--Decoder Networkscited by 32
- 2015Automatic Concept Discovery from Parallel Text and Visual Corporacited by 10
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