# AttnGrounder: Talking to Cars with Attention

arXiv: https://arxiv.org/abs/2009.05684 · HTML: https://mlsanity.com/paper/2009.05684/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2020.

## Built on by (most cited first, 10 of 10)

- [Aligning Cyber Space with Physical World: A Comprehensive Survey on Embodied AI](https://mlsanity.com/paper/2407.06886/) (2024)
- [Multi-View Transformer for 3D Visual Grounding](https://mlsanity.com/paper/2204.02174/) (2022)
- [SAT: 2D Semantics Assisted Training for 3D Visual Grounding](https://mlsanity.com/paper/2105.11450/) (2021)
- [GPT-4 Enhanced Multimodal Grounding for Autonomous Driving: Leveraging Cross-Modal Attention with Large Language Models](https://mlsanity.com/paper/2312.03543/) (2023)
- [Distilling Coarse-to-Fine Semantic Matching Knowledge for Weakly Supervised 3D Visual Grounding](https://mlsanity.com/paper/2307.09267/) (2023)
- [Recent Advances in Multi-modal 3D Intelligence: A Comprehensive Survey and Evaluation](https://mlsanity.com/paper/2310.15676/) (2023)
- [PhiP-G: Physics-Guided Text-to-3D Compositional Scene Generation](https://mlsanity.com/paper/2502.00708/) (2025)
- [Weakly-Supervised 3D Visual Grounding based on Visual Language Alignment](https://mlsanity.com/paper/2312.09625/) (2023)
- [Think Before You Drive: World Model-Inspired Multimodal Grounding for Autonomous Vehicles](https://mlsanity.com/paper/2512.03454/) (2025)
- [E3AD: An Emotion-Aware Vision-Language-Action Model for Human-Centric End-to-End Autonomous Driving](https://mlsanity.com/paper/2512.04733/) (2025)

## Abstract (arXiv)

We propose Attention Grounder (AttnGrounder), a single-stage end-to-end trainable model for the task of visual grounding. Visual grounding aims to localize a specific object in an image based on a given natural language text query. Unlike previous methods that use the same text representation for every image region, we use a visual-text attention module that relates each word in the given query with every region in the corresponding image for constructing a region dependent text representation. Furthermore, for improving the localization ability of our model, we use our visual-text attention module to generate an attention mask around the referred object. The attention mask is trained as an auxiliary task using a rectangular mask generated with the provided ground-truth coordinates. We evaluate AttnGrounder on the Talk2Car dataset and show an improvement of 3.26% over the existing methods.