# Learning to Compose and Reason with Language Tree Structures for Visual Grounding

arXiv: https://arxiv.org/abs/1906.01784 · HTML: https://mlsanity.com/paper/1906.01784/

## Key facts

- Cited by 98 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2019.

## Built on by (most cited first, 30 of 98)

- [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)
- [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)
- [RSVG: Exploring Data and Models for Visual Grounding on Remote Sensing Data](https://mlsanity.com/paper/2210.12634/) (2022)
- [Foundations and Trends in Multimodal Machine Learning: Principles, Challenges, and Open Questions](https://mlsanity.com/paper/2209.03430/) (2022)
- [VLT: Vision-Language Transformer and Query Generation for Referring Segmentation](https://mlsanity.com/paper/2210.15871/) (2022)
- [3D-SPS: Single-Stage 3D Visual Grounding via Referred Point Progressive Selection](https://mlsanity.com/paper/2204.06272/) (2022)
- [Deconfounded Video Moment Retrieval with Causal Intervention](https://mlsanity.com/paper/2106.01534/) (2021)
- [Video Question Answering: Datasets, Algorithms and Challenges](https://mlsanity.com/paper/2203.01225/) (2022)
- [Open-vocabulary Object Segmentation with Diffusion Models](https://mlsanity.com/paper/2301.05221/) (2023)
- [Rotated Multi-Scale Interaction Network for Referring Remote Sensing Image Segmentation](https://mlsanity.com/paper/2312.12470/) (2023)
- [Improving Visual Grounding with Visual-Linguistic Verification and Iterative Reasoning](https://mlsanity.com/paper/2205.00272/) (2022)
- [Shifting More Attention to Visual Backbone: Query-modulated Refinement Networks for End-to-End Visual Grounding](https://mlsanity.com/paper/2203.15442/) (2022)
- [Toward Visual Grounding: A Survey](https://mlsanity.com/paper/2412.20206/) (2024)
- [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)
- [GREC: Generalized Referring Expression Comprehension](https://mlsanity.com/paper/2308.16182/) (2023)
- [One Perturbation is Enough: On Generating Universal Adversarial Perturbations against Vision-Language Pre-training Models](https://mlsanity.com/paper/2406.05491/) (2024)
- [SimVG: A Simple Framework for Visual Grounding with Decoupled Multi-modal Fusion](https://mlsanity.com/paper/2409.17531/) (2024)
- [CLIP-VG: Self-paced Curriculum Adapting of CLIP for Visual Grounding](https://mlsanity.com/paper/2305.08685/) (2023)
- [Language Adaptive Weight Generation for Multi-task Visual Grounding](https://mlsanity.com/paper/2306.04652/) (2023)
- [Pseudo-Q: Generating Pseudo Language Queries for Visual Grounding](https://mlsanity.com/paper/2203.08481/) (2022)
- [HiVG: Hierarchical Multimodal Fine-grained Modulation for Visual Grounding](https://mlsanity.com/paper/2404.13400/) (2024)
- [ReferDINO: Referring Video Object Segmentation with Visual Grounding Foundations](https://mlsanity.com/paper/2501.14607/) (2025)
- [Dynamic MDETR: A Dynamic Multimodal Transformer Decoder for Visual Grounding](https://mlsanity.com/paper/2209.13959/) (2022)
- [Effectiveness Assessment of Recent Large Vision-Language Models](https://mlsanity.com/paper/2403.04306/) (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)

## Abstract (arXiv)

Grounding natural language in images, such as localizing "the black dog on the left of the tree", is one of the core problems in artificial intelligence, as it needs to comprehend the fine-grained and compositional language space. However, existing solutions rely on the association between the holistic language features and visual features, while neglect the nature of compositional reasoning implied in the language. In this paper, we propose a natural language grounding model that can automatically compose a binary tree structure for parsing the language and then perform visual reasoning along the tree in a bottom-up fashion. We call our model RVG-TREE: Recursive Grounding Tree, which is inspired by the intuition that any language expression can be recursively decomposed into two constituent parts, and the grounding confidence score can be recursively accumulated by calculating their grounding scores returned by sub-trees. RVG-TREE can be trained end-to-end by using the Straight-Through Gumbel-Softmax estimator that allows the gradients from the continuous score functions passing through the discrete tree construction. Experiments on several benchmarks show that our model achieves the state-of-the-art performance with more explainable reasoning.