Unsupervised Memorability Modeling from Tip-of-the-Tongue Retrieval Queries

• URL: http://arxiv.org/abs/2511.20854v1
• Date: Tue, 25 Nov 2025 21:02:26 GMT
• Title: Unsupervised Memorability Modeling from Tip-of-the-Tongue Retrieval Queries
• Abstract summary: We introduce the first large-scale unsupervised dataset designed explicitly for modeling visual memorability signals.<n>We leverage tip-of-the-tongue (ToT) retrieval queries from online platforms such as Reddit.<n>Large vision-language models fine-tuned on our dataset outperform state-of-the-art models such as GPT-4o in generating open-ended memorability descriptions.
• Score: 11.240888756303086
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Visual content memorability has intrigued the scientific community for decades, with applications ranging widely, from understanding nuanced aspects of human memory to enhancing content design. A significant challenge in progressing the field lies in the expensive process of collecting memorability annotations from humans. This limits the diversity and scalability of datasets for modeling visual content memorability. Most existing datasets are limited to collecting aggregate memorability scores for visual content, not capturing the nuanced memorability signals present in natural, open-ended recall descriptions. In this work, we introduce the first large-scale unsupervised dataset designed explicitly for modeling visual memorability signals, containing over 82,000 videos, accompanied by descriptive recall data. We leverage tip-of-the-tongue (ToT) retrieval queries from online platforms such as Reddit. We demonstrate that our unsupervised dataset provides rich signals for two memorability-related tasks: recall generation and ToT retrieval. Large vision-language models fine-tuned on our dataset outperform state-of-the-art models such as GPT-4o in generating open-ended memorability descriptions for visual content. We also employ a contrastive training strategy to create the first model capable of performing multimodal ToT retrieval. Our dataset and models present a novel direction, facilitating progress in visual content memorability research.

Related papers

• Understanding Museum Exhibits using Vision-Language Reasoning [52.35301212718003]
  Museums serve as repositories of cultural heritage and historical artifacts from diverse epochs, civilizations, and regions.<n> domain-specific models are essential for interactive query resolution and gaining historical insights.<n>We collect and curate a large-scale dataset of 65M images and 200M question-answer pairs for exhibits from all around the world.
  arXiv  Detail & Related papers  (2024-12-02T10:54:31Z)
• Web-Scale Visual Entity Recognition: An LLM-Driven Data Approach [56.55633052479446]
  Web-scale visual entity recognition presents significant challenges due to the lack of clean, large-scale training data. We propose a novel methodology to curate such a dataset, leveraging a multimodal large language model (LLM) for label verification, metadata generation, and rationale explanation. Experiments demonstrate that models trained on this automatically curated data achieve state-of-the-art performance on web-scale visual entity recognition tasks.
  arXiv  Detail & Related papers  (2024-10-31T06:55:24Z)
• Data-efficient Large Vision Models through Sequential Autoregression [58.26179273091461]
  We develop an efficient, autoregression-based vision model on a limited dataset. We demonstrate how this model achieves proficiency in a spectrum of visual tasks spanning both high-level and low-level semantic understanding. Our empirical evaluations underscore the model's agility in adapting to various tasks, heralding a significant reduction in the parameter footprint.
  arXiv  Detail & Related papers  (2024-02-07T13:41:53Z)
• Sequential Modeling Enables Scalable Learning for Large Vision Models [120.91839619284431]
  We introduce a novel sequential modeling approach which enables learning a Large Vision Model (LVM) without making use of any linguistic data. We define a common format, "visual sentences", in which we can represent raw images and videos as well as annotated data sources.
  arXiv  Detail & Related papers  (2023-12-01T18:59:57Z)
• Visual Data-Type Understanding does not emerge from Scaling Vision-Language Models [31.69213233651326]
  We introduce the novel task of Visual Data-Type Identification. An extensive zero-shot evaluation of 39 vision-language models (VLMs) shows a nuanced performance landscape.
  arXiv  Detail & Related papers  (2023-10-12T17:59:30Z)
• StableLLaVA: Enhanced Visual Instruction Tuning with Synthesized Image-Dialogue Data [129.92449761766025]
  We propose a novel data collection methodology that synchronously synthesizes images and dialogues for visual instruction tuning. This approach harnesses the power of generative models, marrying the abilities of ChatGPT and text-to-image generative models. Our research includes comprehensive experiments conducted on various datasets.
  arXiv  Detail & Related papers  (2023-08-20T12:43:52Z)
• Unified Visual Relationship Detection with Vision and Language Models [89.77838890788638]
  This work focuses on training a single visual relationship detector predicting over the union of label spaces from multiple datasets. We propose UniVRD, a novel bottom-up method for Unified Visual Relationship Detection by leveraging vision and language models. Empirical results on both human-object interaction detection and scene-graph generation demonstrate the competitive performance of our model.
  arXiv  Detail & Related papers  (2023-03-16T00:06:28Z)
• Visual Distant Supervision for Scene Graph Generation [66.10579690929623]
  Scene graph models usually require supervised learning on large quantities of labeled data with intensive human annotation. We propose visual distant supervision, a novel paradigm of visual relation learning, which can train scene graph models without any human-labeled data. Comprehensive experimental results show that our distantly supervised model outperforms strong weakly supervised and semi-supervised baselines.
  arXiv  Detail & Related papers  (2021-03-29T06:35:24Z)
• Reducing Overlearning through Disentangled Representations by Suppressing Unknown Tasks [8.517620051440005]
  Existing deep learning approaches for learning visual features tend to overlearn and extract more information than what is required for the task at hand. From a privacy preservation perspective, the input visual information is not protected from the model. We propose a model-agnostic solution for reducing model overlearning by suppressing all the unknown tasks.
  arXiv  Detail & Related papers  (2020-05-20T17:31:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.