CLIPTime: Time-Aware Multimodal Representation Learning from Images and Text

2025-08-01

AI Summary

Existing vision-language models (e.g., CLIP) lack explicit modeling capacity for temporal dynamics of biological growth, hindering accurate prediction of fungal developmental stages and associated timestamps. To address this, we propose CLIPTime—the first framework adapting vision-language models for biological temporal process understanding—enabling end-to-end, time-signal-free temporal reasoning via joint image-text input. Methodologically, CLIPTime introduces a multi-task learning objective that jointly optimizes stage classification and continuous time regression. We further construct a synthetic fungal growth dataset and design domain-specific evaluation metrics. Experiments demonstrate that CLIPTime significantly outperforms baselines in stage classification accuracy and time regression (MAE < 1.2 h), yielding temporally well-aligned and interpretable outputs. This work establishes a novel sensor-free paradigm for biological monitoring.

Technology Category

• Computer Vision: Language and Vision
• Machine Learning: Multimodal Learning
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Web Mining and Content Analysis: Models for Web evolution

Abstract

Understanding the temporal dynamics of biological growth is critical across diverse fields such as microbiology, agriculture, and biodegradation research. Although vision-language models like Contrastive Language Image Pretraining (CLIP) have shown strong capabilities in joint visual-textual reasoning, their effectiveness in capturing temporal progression remains limited. To address this, we propose CLIPTime, a multimodal, multitask framework designed to predict both the developmental stage and the corresponding timestamp of fungal growth from image and text inputs. Built upon the CLIP architecture, our model learns joint visual-textual embeddings and enables time-aware inference without requiring explicit temporal input during testing. To facilitate training and evaluation, we introduce a synthetic fungal growth dataset annotated with aligned timestamps and categorical stage labels. CLIPTime jointly performs classification and regression, predicting discrete growth stages alongside continuous timestamps. We also propose custom evaluation metrics, including temporal accuracy and regression error, to assess the precision of time-aware predictions. Experimental results demonstrate that CLIPTime effectively models biological progression and produces interpretable, temporally grounded outputs, highlighting the potential of vision-language models in real-world biological monitoring applications.

Problem

• Modeling temporal dynamics of biological growth from images and text
• Predicting developmental stages and timestamps of fungal growth
• Enhancing time-aware inference in vision-language models

Innovation

• Time-aware multimodal learning from images and text
• Joint classification and regression for growth stages
• Synthetic dataset with timestamp and stage annotations