# Progressive Language-guided Visual Learning for Multi-Task Visual Grounding

arXiv: https://arxiv.org/abs/2504.16145 · HTML: https://mlsanity.com/paper/2504.16145/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 40 references that are in the map.
- First posted on arXiv 22 Apr 2025.

## Builds on (most cited first, 29 of 29)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019)
- [Flickr30k Entities: Collecting Region-to-Phrase Correspondences for Richer Image-to-Sentence Models](https://mlsanity.com/paper/1505.04870/) (2015)
- [Modeling Context in Referring Expressions](https://mlsanity.com/paper/1608.00272/) (2016)
- [Generation and Comprehension of Unambiguous Object Descriptions](https://mlsanity.com/paper/1511.02283/) (2015)
- [Exploring Plain Vision Transformer Backbones for Object Detection](https://mlsanity.com/paper/2203.16527/) (2022)
- [TransVG: End-to-End Visual Grounding with Transformers](https://mlsanity.com/paper/2104.08541/) (2021)
- [Modeling Context Between Objects for Referring Expression Understanding](https://mlsanity.com/paper/1608.00525/) (2016)
- [Multi-task Collaborative Network for Joint Referring Expression Comprehension and Segmentation](https://mlsanity.com/paper/2003.08813/) (2020)
- [A Fast and Accurate One-Stage Approach to Visual Grounding](https://mlsanity.com/paper/1908.06354/) (2019)
- [PolyFormer: Referring Image Segmentation as Sequential Polygon Generation](https://mlsanity.com/paper/2302.07387/) (2023)
- [Referring Transformer: A One-step Approach to Multi-task Visual Grounding](https://mlsanity.com/paper/2106.03089/) (2021)
- [SeqTR: A Simple yet Universal Network for Visual Grounding](https://mlsanity.com/paper/2203.16265/) (2022)
- [Improving One-stage Visual Grounding by Recursive Sub-query Construction](https://mlsanity.com/paper/2008.01059/) (2020)
- [A Real-Time Cross-modality Correlation Filtering Method for Referring Expression Comprehension](https://mlsanity.com/paper/1909.07072/) (2019)
- [Encoder Fusion Network with Co-Attention Embedding for Referring Image Segmentation](https://mlsanity.com/paper/2105.01839/) (2021)
- [Learning to Compose and Reason with Language Tree Structures for Visual Grounding](https://mlsanity.com/paper/1906.01784/) (2019)
- [Locate then Segment: A Strong Pipeline for Referring Image Segmentation](https://mlsanity.com/paper/2103.16284/) (2021)
- [Grounding Referring Expressions in Images by Variational Context](https://mlsanity.com/paper/1712.01892/) (2017)
- [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)
- [CLIP-VG: Self-paced Curriculum Adapting of CLIP for Visual Grounding](https://mlsanity.com/paper/2305.08685/) (2023)
- [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)
- [An Efficient and Effective Transformer Decoder-Based Framework for Multi-Task Visual Grounding](https://mlsanity.com/paper/2408.01120/) (2024)
- [SegVG: Transferring Object Bounding Box to Segmentation for Visual Grounding](https://mlsanity.com/paper/2407.03200/) (2024)

## Built on by (most cited first, 3 of 3)

- [Multimodal Referring Segmentation: A Survey](https://mlsanity.com/paper/2508.00265/) (2025)
- [Unlocking the Potential of MLLMs in Referring Expression Segmentation via a Light-weight Mask Decoder](https://mlsanity.com/paper/2508.04107/) (2025)
- [BARE: Towards Bias-Aware and Reasoning-Enhanced One-Tower Visual Grounding](https://mlsanity.com/paper/2601.01526/) (2026)

## Abstract (arXiv)

Multi-task visual grounding (MTVG) includes two sub-tasks, i.e., Referring Expression Comprehension (REC) and Referring Expression Segmentation (RES). The existing representative approaches generally follow the research pipeline which mainly consists of three core procedures, including independent feature extraction for visual and linguistic modalities, respectively, cross-modal interaction module, and independent prediction heads for different sub-tasks. Albeit achieving remarkable performance, this research line has two limitations: 1) The linguistic content has not been fully injected into the entire visual backbone for boosting more effective visual feature extraction and it needs an extra cross-modal interaction module; 2) The relationship between REC and RES tasks is not effectively exploited to help the collaborative prediction for more accurate output. To deal with these problems, in this paper, we propose a Progressive Language-guided Visual Learning framework for multi-task visual grounding, called PLVL, which not only finely mine the inherent feature expression of the visual modality itself but also progressively inject the language information to help learn linguistic-related visual features. In this manner, our PLVL does not need additional cross-modal fusion module while fully introducing the language guidance. Furthermore, we analyze that the localization center for REC would help identify the to-be-segmented object region for RES to some extent. Inspired by this investigation, we design a multi-task head to accomplish collaborative predictions for these two sub-tasks. Extensive experiments conducted on several benchmark datasets comprehensively substantiate that our PLVL obviously outperforms the representative methods in both REC and RES tasks. https://github.com/jcwang0602/PLVL