# Unsupervised Textual Grounding: Linking Words to Image Concepts

arXiv: https://arxiv.org/abs/1803.11185 · HTML: https://mlsanity.com/paper/1803.11185/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Mar 2018.

## Built on by (most cited first, 13 of 13)

- [GroupViT: Semantic Segmentation Emerges from Text Supervision](https://mlsanity.com/paper/2202.11094/) (2022)
- [Toward Visual Grounding: A Survey](https://mlsanity.com/paper/2412.20206/) (2024)
- [Relation-aware Instance Refinement for Weakly Supervised Visual Grounding](https://mlsanity.com/paper/2103.12989/) (2021)
- [CLIP-VG: Self-paced Curriculum Adapting of CLIP for Visual Grounding](https://mlsanity.com/paper/2305.08685/) (2023)
- [Pseudo-Q: Generating Pseudo Language Queries for Visual Grounding](https://mlsanity.com/paper/2203.08481/) (2022)
- [Causal Reasoning Meets Visual Representation Learning: A Prospective Study](https://mlsanity.com/paper/2204.12037/) (2022)
- [Improving Visual Grounding by Encouraging Consistent Gradient-based Explanations](https://mlsanity.com/paper/2206.15462/) (2022)
- [Adapting CLIP For Phrase Localization Without Further Training](https://mlsanity.com/paper/2204.03647/) (2022)
- [Unpaired Referring Expression Grounding via Bidirectional Cross-Modal Matching](https://mlsanity.com/paper/2201.06686/) (2022)
- [Med-GLIP: Advancing Medical Language-Image Pre-training with Large-scale Grounded Dataset](https://mlsanity.com/paper/2508.10528/) (2025)
- [3DWG: 3D Weakly Supervised Visual Grounding via Category and Instance-Level Alignment](https://mlsanity.com/paper/2505.01809/) (2025)
- [Transformer-based Spatial Grounding: A Comprehensive Survey](https://mlsanity.com/paper/2507.12739/) (2025)
- [Prototype-Aware Multimodal Alignment for Open-Vocabulary Visual Grounding](https://mlsanity.com/paper/2509.06291/) (2025)

## Abstract (arXiv)

Textual grounding, i.e., linking words to objects in images, is a challenging but important task for robotics and human-computer interaction. Existing techniques benefit from recent progress in deep learning and generally formulate the task as a supervised learning problem, selecting a bounding box from a set of possible options. To train these deep net based approaches, access to a large-scale datasets is required, however, constructing such a dataset is time-consuming and expensive. Therefore, we develop a completely unsupervised mechanism for textual grounding using hypothesis testing as a mechanism to link words to detected image concepts. We demonstrate our approach on the ReferIt Game dataset and the Flickr30k data, outperforming baselines by 7.98% and 6.96% respectively.