Cycled Compositional Learning between Images and Text
Not on the map. Why?
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.
Key facts. Cited by 1 paper 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
11 of 14 references.
References · 11
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 7,005
- 2016Densely Connected Convolutional Networkscited by 6,720
- 2018Composing Text and Image for Image Retrieval - An Empirical Odysseycited by 236
- 2017MUTAN: Multimodal Tucker Fusion for Visual Question Answeringcited by 137
- 2017Learnable pooling with Context Gating for video classificationcited by 58
- 2019Fashion IQ: A New Dataset Towards Retrieving Images by Natural Language Feedbackcited by 43
- 2020CurlingNet: Compositional Learning between Images and Text for Fashion IQ Datacited by 21
- 2019FishNet: A Versatile Backbone for Image, Region, and Pixel Level Predictioncited by 16
- 2019Designovel's system description for Fashion-IQ challenge 2019cited by 2
Built on by
Citing papers per year: 2022: 1.
1 paper.