# Cycled Compositional Learning between Images and Text

arXiv: https://arxiv.org/abs/2107.11509 · HTML: https://mlsanity.com/paper/2107.11509/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 14 references that are in the map.
- First posted on arXiv 24 Jul 2021.

## Builds on (most cited first, 11 of 11)

- [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)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Composing Text and Image for Image Retrieval - An Empirical Odyssey](https://mlsanity.com/paper/1812.07119/) (2018)
- [MUTAN: Multimodal Tucker Fusion for Visual Question Answering](https://mlsanity.com/paper/1705.06676/) (2017)
- [Learnable pooling with Context Gating for video classification](https://mlsanity.com/paper/1706.06905/) (2017)
- [Fashion IQ: A New Dataset Towards Retrieving Images by Natural Language Feedback](https://mlsanity.com/paper/1905.12794/) (2019)
- [CurlingNet: Compositional Learning between Images and Text for Fashion IQ Data](https://mlsanity.com/paper/2003.12299/) (2020)
- [FishNet: A Versatile Backbone for Image, Region, and Pixel Level Prediction](https://mlsanity.com/paper/1901.03495/) (2019)
- [Designovel's system description for Fashion-IQ challenge 2019](https://mlsanity.com/paper/1910.11119/) (2019)

## Built on by (most cited first, 1 of 1)

- [Image Search with Text Feedback by Additive Attention Compositional Learning](https://mlsanity.com/paper/2203.03809/) (2022)

## Abstract (arXiv)

We present an approach named the Cycled Composition Network that can measure the semantic distance of the composition of image-text embedding. First, the Composition Network transit a reference image to target image in an embedding space using relative caption. Second, the Correction Network calculates a difference between reference and retrieved target images in the embedding space and match it with a relative caption. Our goal is to learn a Composition mapping with the Composition Network. Since this one-way mapping is highly under-constrained, we couple it with an inverse relation learning with the Correction Network and introduce a cycled relation for given Image We participate in Fashion IQ 2020 challenge and have won the first place with the ensemble of our model.