# Improving Referring Expression Grounding with Cross-modal Attention-guided Erasing

arXiv: https://arxiv.org/abs/1903.00839 · HTML: https://mlsanity.com/paper/1903.00839/

## Key facts

- Cited by 88 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Mar 2019.

## Built on by (most cited first, 30 of 88)

- [TransVG: End-to-End Visual Grounding with Transformers](https://mlsanity.com/paper/2104.08541/) (2021)
- [Universal Instance Perception as Object Discovery and Retrieval](https://mlsanity.com/paper/2303.06674/) (2023)
- [VLP: A Survey on Vision-Language Pre-training](https://mlsanity.com/paper/2202.09061/) (2022)
- [Referring Transformer: A One-step Approach to Multi-task Visual Grounding](https://mlsanity.com/paper/2106.03089/) (2021)
- [InstanceRefer: Cooperative Holistic Understanding for Visual Grounding on Point Clouds through Instance Multi-level Contextual Referring](https://mlsanity.com/paper/2103.01128/) (2021)
- [SeqTR: A Simple yet Universal Network for Visual Grounding](https://mlsanity.com/paper/2203.16265/) (2022)
- [RSVG: Exploring Data and Models for Visual Grounding on Remote Sensing Data](https://mlsanity.com/paper/2210.12634/) (2022)
- [TubeDETR: Spatio-Temporal Video Grounding with Transformers](https://mlsanity.com/paper/2203.16434/) (2022)
- [Shifting More Attention to Visual Backbone: Query-modulated Refinement Networks for End-to-End Visual Grounding](https://mlsanity.com/paper/2203.15442/) (2022)
- [Look Before You Leap: Learning Landmark Features for One-Stage Visual Grounding](https://mlsanity.com/paper/2104.04386/) (2021)
- [Toward Visual Grounding: A Survey](https://mlsanity.com/paper/2412.20206/) (2024)
- [Attention, please! A survey of Neural Attention Models in Deep Learning](https://mlsanity.com/paper/2103.16775/) (2021)
- [Scene-Intuitive Agent for Remote Embodied Visual Grounding](https://mlsanity.com/paper/2103.12944/) (2021)
- [Visual Grounding with Transformers](https://mlsanity.com/paper/2105.04281/) (2021)
- [TransVG++: End-to-End Visual Grounding with Language Conditioned Vision Transformer](https://mlsanity.com/paper/2206.06619/) (2022)
- [Embracing Consistency: A One-Stage Approach for Spatio-Temporal Video Grounding](https://mlsanity.com/paper/2209.13306/) (2022)
- [Local-Global Context Aware Transformer for Language-Guided Video Segmentation](https://mlsanity.com/paper/2203.09773/) (2022)
- [SimVG: A Simple Framework for Visual Grounding with Decoupled Multi-modal Fusion](https://mlsanity.com/paper/2409.17531/) (2024)
- [Cross-Modal Discrete Representation Learning](https://mlsanity.com/paper/2106.05438/) (2021)
- [Language Adaptive Weight Generation for Multi-task Visual Grounding](https://mlsanity.com/paper/2306.04652/) (2023)
- [Refer-it-in-RGBD: A Bottom-up Approach for 3D Visual Grounding in RGBD Images](https://mlsanity.com/paper/2103.07894/) (2021)
- [YouRefIt: Embodied Reference Understanding with Language and Gesture](https://mlsanity.com/paper/2109.03413/) (2021)
- [HiVG: Hierarchical Multimodal Fine-grained Modulation for Visual Grounding](https://mlsanity.com/paper/2404.13400/) (2024)
- [Parallel Vertex Diffusion for Unified Visual Grounding](https://mlsanity.com/paper/2303.07216/) (2023)
- [OneRef: Unified One-tower Expression Grounding and Segmentation with Mask Referring Modeling](https://mlsanity.com/paper/2410.08021/) (2024)
- [Detecting Human-Object Interactions with Object-Guided Cross-Modal Calibrated Semantics](https://mlsanity.com/paper/2202.00259/) (2022)
- [Referring Image Matting](https://mlsanity.com/paper/2206.05149/) (2022)
- [Entity-enhanced Adaptive Reconstruction Network for Weakly Supervised Referring Expression Grounding](https://mlsanity.com/paper/2207.08386/) (2022)
- [Mono3DVG: 3D Visual Grounding in Monocular Images](https://mlsanity.com/paper/2312.08022/) (2023)
- [Multimodal Referring Segmentation: A Survey](https://mlsanity.com/paper/2508.00265/) (2025)

## Abstract (arXiv)

Referring expression grounding aims at locating certain objects or persons in an image with a referring expression, where the key challenge is to comprehend and align various types of information from visual and textual domain, such as visual attributes, location and interactions with surrounding regions. Although the attention mechanism has been successfully applied for cross-modal alignments, previous attention models focus on only the most dominant features of both modalities, and neglect the fact that there could be multiple comprehensive textual-visual correspondences between images and referring expressions. To tackle this issue, we design a novel cross-modal attention-guided erasing approach, where we discard the most dominant information from either textual or visual domains to generate difficult training samples online, and to drive the model to discover complementary textual-visual correspondences. Extensive experiments demonstrate the effectiveness of our proposed method, which achieves state-of-the-art performance on three referring expression grounding datasets.