# Intra-Modal Constraint Loss For Image-Text Retrieval

arXiv: https://arxiv.org/abs/2207.05024 · HTML: https://mlsanity.com/paper/2207.05024/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 24 references that are in the map.
- First posted on arXiv 11 Jul 2022.

## Builds on (most cited first, 10 of 10)

- [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)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Deep Visual-Semantic Alignments for Generating Image Descriptions](https://mlsanity.com/paper/1412.2306/) (2014)
- [Dual-Path Convolutional Image-Text Embeddings with Instance Loss](https://mlsanity.com/paper/1711.05535/) (2017)
- [Learning Semantic Concepts and Order for Image and Sentence Matching](https://mlsanity.com/paper/1712.02036/) (2017)
- [Ladder Loss for Coherent Visual-Semantic Embedding](https://mlsanity.com/paper/1911.07528/) (2019)
- [A Strong and Robust Baseline for Text-Image Matching](https://mlsanity.com/paper/1906.01205/) (2019)
- [Leveraging Visual Question Answering for Image-Caption Ranking](https://mlsanity.com/paper/1605.01379/) (2016)

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

- [Cross-Modal Retrieval for Motion and Text via DropTriple Loss](https://mlsanity.com/paper/2305.04195/) (2023)

## Abstract (arXiv)

Cross-modal retrieval has drawn much attention in both computer vision and natural language processing domains. With the development of convolutional and recurrent neural networks, the bottleneck of retrieval across image-text modalities is no longer the extraction of image and text features but an efficient loss function learning in embedding space. Many loss functions try to closer pairwise features from heterogeneous modalities. This paper proposes a method for learning joint embedding of images and texts using an intra-modal constraint loss function to reduce the violation of negative pairs from the same homogeneous modality. Experimental results show that our approach outperforms state-of-the-art bi-directional image-text retrieval methods on Flickr30K and Microsoft COCO datasets. Our code is publicly available: https://github.com/CanonChen/IMC.