# Pseudo-Q: Generating Pseudo Language Queries for Visual Grounding

arXiv: https://arxiv.org/abs/2203.08481 · HTML: https://mlsanity.com/paper/2203.08481/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- Builds on 63 of its 75 references that are in the map.
- First posted on arXiv 16 Mar 2022.

## Builds on (most cited first, 30 of 63)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations](https://mlsanity.com/paper/1602.07332/) (2016)
- [VQA: Visual Question Answering](https://mlsanity.com/paper/1505.00468/) (2015)
- [ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks](https://mlsanity.com/paper/1908.02265/) (2019)
- [LXMERT: Learning Cross-Modality Encoder Representations from Transformers](https://mlsanity.com/paper/1908.07490/) (2019)
- [Flickr30k Entities: Collecting Region-to-Phrase Correspondences for Richer Image-to-Sentence Models](https://mlsanity.com/paper/1505.04870/) (2015)
- [Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering](https://mlsanity.com/paper/1707.07998/) (2017)
- [Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks](https://mlsanity.com/paper/2004.06165/) (2020)
- [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)
- [Segmenter: Transformer for Semantic Segmentation](https://mlsanity.com/paper/2105.05633/) (2021)
- [From Recognition to Cognition: Visual Commonsense Reasoning](https://mlsanity.com/paper/1811.10830/) (2018)
- [MAttNet: Modular Attention Network for Referring Expression Comprehension](https://mlsanity.com/paper/1801.08186/) (2018)
- [TransVG: End-to-End Visual Grounding with Transformers](https://mlsanity.com/paper/2104.08541/) (2021)
- [A Fast and Accurate One-Stage Approach to Visual Grounding](https://mlsanity.com/paper/1908.06354/) (2019)
- [Zero-Shot Object Detection](https://mlsanity.com/paper/1804.04340/) (2018)
- [CPT: Colorful Prompt Tuning for Pre-trained Vision-Language Models](https://mlsanity.com/paper/2109.11797/) (2021)
- [Learning to Assemble Neural Module Tree Networks for Visual Grounding](https://mlsanity.com/paper/1812.03299/) (2018)

## Built on by (most cited first, 30 of 33)

- [What does CLIP know about a red circle? Visual prompt engineering for VLMs](https://mlsanity.com/paper/2304.06712/) (2023)
- [RSVG: Exploring Data and Models for Visual Grounding on Remote Sensing Data](https://mlsanity.com/paper/2210.12634/) (2022)
- [From Images to Textual Prompts: Zero-shot VQA with Frozen Large Language Models](https://mlsanity.com/paper/2212.10846/) (2022)
- [Not All Features Matter: Enhancing Few-shot CLIP with Adaptive Prior Refinement](https://mlsanity.com/paper/2304.01195/) (2023)
- [REVIVE: Regional Visual Representation Matters in Knowledge-Based Visual Question Answering](https://mlsanity.com/paper/2206.01201/) (2022)
- [Toward Visual Grounding: A Survey](https://mlsanity.com/paper/2412.20206/) (2024)
- [CLIP-VG: Self-paced Curriculum Adapting of CLIP for Visual Grounding](https://mlsanity.com/paper/2305.08685/) (2023)
- [Referring Image Segmentation Using Text Supervision](https://mlsanity.com/paper/2308.14575/) (2023)
- [Learning Visual Grounding from Generative Vision and Language Model](https://mlsanity.com/paper/2407.14563/) (2024)
- [Context Disentangling and Prototype Inheriting for Robust Visual Grounding](https://mlsanity.com/paper/2312.11967/) (2023)
- [Language-free Training for Zero-shot Video Grounding](https://mlsanity.com/paper/2210.12977/) (2022)
- [Surgical-VQLA++: Adversarial Contrastive Learning for Calibrated Robust Visual Question-Localized Answering in Robotic Surgery](https://mlsanity.com/paper/2408.04958/) (2024)
- [Cardiac Copilot: Automatic Probe Guidance for Echocardiography with World Model](https://mlsanity.com/paper/2406.13165/) (2024)
- [SMAUG: Sparse Masked Autoencoder for Efficient Video-Language Pre-training](https://mlsanity.com/paper/2211.11446/) (2022)
- [Pseudo-RIS: Distinctive Pseudo-supervision Generation for Referring Image Segmentation](https://mlsanity.com/paper/2407.07412/) (2024)
- [Structure-aware World Model for Probe Guidance via Large-scale Self-supervised Pre-train](https://mlsanity.com/paper/2406.19756/) (2024)
- [Modal-specific Pseudo Query Generation for Video Corpus Moment Retrieval](https://mlsanity.com/paper/2210.12617/) (2022)
- [SafaRi:Adaptive Sequence Transformer for Weakly Supervised Referring Expression Segmentation](https://mlsanity.com/paper/2407.02389/) (2024)
- [ResVG: Enhancing Relation and Semantic Understanding in Multiple Instances for Visual Grounding](https://mlsanity.com/paper/2408.16314/) (2024)
- [A Unified Mutual Supervision Framework for Referring Expression Segmentation and Generation](https://mlsanity.com/paper/2211.07919/) (2022)
- [Zero-Shot Dense Video Captioning by Jointly Optimizing Text and Moment](https://mlsanity.com/paper/2307.02682/) (2023)
- [GVCCI: Lifelong Learning of Visual Grounding for Language-Guided Robotic Manipulation](https://mlsanity.com/paper/2307.05963/) (2023)
- [Commonsense for Zero-Shot Natural Language Video Localization](https://mlsanity.com/paper/2312.17429/) (2023)
- [Learning from Synthetic Data for Visual Grounding](https://mlsanity.com/paper/2403.13804/) (2024)
- [Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding](https://mlsanity.com/paper/2505.15123/) (2025)
- [Think Before You Drive: World Model-Inspired Multimodal Grounding for Autonomous Vehicles](https://mlsanity.com/paper/2512.03454/) (2025)
- [Paint Outside the Box: Synthesizing and Selecting Training Data for Visual Grounding](https://mlsanity.com/paper/2412.00684/) (2024)
- [AlignCAT: Visual-Linguistic Alignment of Category and Attribute for Weakly Supervised Visual Grounding](https://mlsanity.com/paper/2508.03201/) (2025)
- [Synthetic Object Compositions for Scalable and Accurate Learning in Detection, Segmentation, and Grounding](https://mlsanity.com/paper/2510.09110/) (2025)
- [Prototype-Aware Multimodal Alignment for Open-Vocabulary Visual Grounding](https://mlsanity.com/paper/2509.06291/) (2025)

## Abstract (arXiv)

Visual grounding, i.e., localizing objects in images according to natural language queries, is an important topic in visual language understanding. The most effective approaches for this task are based on deep learning, which generally require expensive manually labeled image-query or patch-query pairs. To eliminate the heavy dependence on human annotations, we present a novel method, named Pseudo-Q, to automatically generate pseudo language queries for supervised training. Our method leverages an off-the-shelf object detector to identify visual objects from unlabeled images, and then language queries for these objects are obtained in an unsupervised fashion with a pseudo-query generation module. Then, we design a task-related query prompt module to specifically tailor generated pseudo language queries for visual grounding tasks. Further, in order to fully capture the contextual relationships between images and language queries, we develop a visual-language model equipped with multi-level cross-modality attention mechanism. Extensive experimental results demonstrate that our method has two notable benefits: (1) it can reduce human annotation costs significantly, e.g., 31% on RefCOCO without degrading original model's performance under the fully supervised setting, and (2) without bells and whistles, it achieves superior or comparable performance compared to state-of-the-art weakly-supervised visual grounding methods on all the five datasets we have experimented. Code is available at https://github.com/LeapLabTHU/Pseudo-Q.