mlsanityData to 14 Oct 2026

Are Elephants Bigger than Butterflies? Reasoning about Sizes of Objects

arXiv 1602.00753 ↗First posted 2 Feb 2016cs.AI cs.CVcited by 22

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Abstract (arXiv)

Human vision greatly benefits from the information about sizes of objects. The role of size in several visual reasoning tasks has been thoroughly explored in human perception and cognition. However, the impact of the information about sizes of objects is yet to be determined in AI. We postulate that this is mainly attributed to the lack of a comprehensive repository of size information. In this paper, we introduce a method to automatically infer object sizes, leveraging visual and textual information from web. By maximizing the joint likelihood of textual and visual observations, our method learns reliable relative size estimates, with no explicit human supervision. We introduce the relative size dataset and show that our method outperforms competitive textual and visual baselines in reasoning about size comparisons.

Key facts. Cited by 22 papers in the map (data through 14 Oct 2026). First posted on arXiv 2 Feb 2016.

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Citing papers per year: 2021: 4; 2022: 3; 2023: 5; 2024: 5; 2025: 4; 2026: 1.

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  1. 2023Language Is Not All You Need: Aligning Perception with Language Modelscited by 543
  2. 2022Things not Written in Text: Exploring Spatial Commonsense from Visual Signalscited by 41
  3. 2021Back to Square One: Artifact Detection, Training and Commonsense Disentanglement in the Winograd Schemacited by 32
  4. 2023Learning Visual Representations via Language-Guided Samplingcited by 32
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  6. 2025SpaCE-10: A Comprehensive Benchmark for Multimodal Large Language Models in Compositional Spatial Intelligencecited by 23
  7. 2023Mitigating Temporal Misalignment by Discarding Outdated Factscited by 21
  8. 2022Visually-Augmented Language Modelingcited by 18
  9. 2021Did the Cat Drink the Coffee? Challenging Transformers with Generalized Event Knowledgecited by 15
  10. 2023NeuroComparatives: Neuro-Symbolic Distillation of Comparative Knowledgecited by 8
  11. 2021What's the best place for an AI conference, Vancouver or ______: Why completing comparative questions is difficultcited by 4
  12. 2023ImageNetVC: Zero- and Few-Shot Visual Commonsense Evaluation on 1000 ImageNet Categoriescited by 4
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  2. 2021Inferring spatial relations from textual descriptions of imagescited by 1
  3. 2024Grounding Spatial Relations in Text-Only Language Modelscited by 1
  4. 2024LaMI: Augmenting Large Language Models via Late Multi-Image Fusioncited by 1
  5. 2022Semantic Novelty Detection and Characterization in Factual Text Involving Named Entitiescited by 0
  6. 2024Plausible-Parrots @ MSP2023: Enhancing Semantic Plausibility Modeling using Entity and Event Knowledgecited by 0
  7. 2024Improving the Efficiency of Visually Augmented Language Modelscited by 0
  8. 2025Plausibility Vaccine: Injecting LLM Knowledge for Event Plausibilitycited by 0
  9. 2025TComQA: Extracting Temporal Commonsense from Textcited by 0
  10. 2026CALIPER: Metric-Grounded Model-Free Recognition of Visually Similar Industrial Partscited by 0