Blind Dates: Examining the Expression of Temporality in Historical Photographs
This paper explores the capacity of computer vision models to discern temporal information in visual content, focusing specifically on historical photographs. We investigate the dating of images using OpenCLIP, an open-source implementation of CLIP, a multi-modal language and vision model. Our experiment consists of three steps: zero-shot classification, fine-tuning, and analysis of visual content. We use the \textit{De Boer Scene Detection} dataset, containing 39,866 gray-scale historical press photographs from 1950 to 1999. The results show that zero-shot classification is relatively ineffective for image dating, with a bias towards predicting dates in the past. Fine-tuning OpenCLIP with a logistic classifier improves performance and eliminates the bias. Additionally, our analysis reveals that images featuring buses, cars, cats, dogs, and people are more accurately dated, suggesting the presence of temporal markers. The study highlights the potential of machine learning models like OpenCLIP in dating images and emphasizes the importance of fine-tuning for accurate temporal analysis. Future research should explore the application of these findings to color photographs and diverse datasets.
Code (0)
등록된 구현이 없습니다.
Tasks
zero-shot-classificationZero-Shot LearningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
A Study of Temporal Fusion Strategies for Named Entity Recognition in Historical Texts
Temporal variation poses a unique challenge for named entity recognition (NER) in historical texts, where entities drift in surface form and salience across time. While language models (LMs) have made progress in various…
Examining Temporality in Document Classification
Many corpora span broad periods of time. Language processing models trained during one time period may not work well in future time periods, and the best model may depend on specific times of year (e.g., people might des…
ClassificationDocument ClassificationDomain AdaptationGeneral ClassificationClinicalBench: Stress-Testing Assertion-Aware Retrieval for Cross-Admission Clinical QA on MIMIC-IV
Reasoning benchmarks measure clinical performance on clean inputs. We evaluate the step before reasoning: retrieval over real EHR notes, where negation, temporality, and family-versus-patient attribution can flip a corre…
ATM: Action Temporality Modeling for Video Question Answering
Despite significant progress in video question answering (VideoQA), existing methods fall short of questions that require causal/temporal reasoning across frames. This can be attributed to imprecise motion representation…
Contrastive LearningOptical Flow EstimationQuestion AnsweringVideo Question AnsweringAn Analytics of Culture: Modeling Subjectivity, Scalability, Contextuality, and Temporality
There is a bidirectional relationship between culture and AI; AI models are increasingly used to analyse culture, thereby shaping our understanding of culture. On the other hand, the models are trained on collections of …
Cultural Vocal Bursts Intensity Prediction