VQD: Visual Query Detection in Natural Scenes
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Abstract (arXiv)
We propose Visual Query Detection (VQD), a new visual grounding task. In VQD, a system is guided by natural language to localize a variable number of objects in an image. VQD is related to visual referring expression recognition, where the task is to localize only one object. We describe the first dataset for VQD and we propose baseline algorithms that demonstrate the difficulty of the task compared to referring expression recognition.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Apr 2019.
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Citing papers per year: 2021: 2; 2022: 0; 2023: 1; 2024: 2; 2025: 1; 2026: 1.
7 papers.
- 2021QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehensioncited by 91
- 2023Overcoming the Stability Gap in Continual Learningcited by 13
- 2024What Variables Affect Out-of-Distribution Generalization in Pretrained Models?cited by 13
- 2021Selective Replay Enhances Learning in Online Continual Analogical Reasoningcited by 12
- 2025Calibrating Uncertainty Quantification of Multi-Modal LLMs using Groundingcited by 8
- 2024Revisiting Multi-Modal LLM Evaluationcited by 5
- 2026UniRef-UAV: A Multimodal Benchmark for Universal Referring in UAV Imagerycited by 0
- 2026UniRef-UAV: A Multimodal Benchmark for Universal Referring in UAV Imagery9 Jul 2026
- 2025Calibrating Uncertainty Quantification of Multi-Modal LLMs using Grounding30 Apr 2025
- 2024Revisiting Multi-Modal LLM Evaluation9 Aug 2024
- 2024What Variables Affect Out-of-Distribution Generalization in Pretrained Models?23 May 2024
- 2023Overcoming the Stability Gap in Continual Learning2 Jun 2023
- 2021QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehension27 Jul 2021
- 2021Selective Replay Enhances Learning in Online Continual Analogical Reasoning6 Mar 2021