Contrastive Language Image Pre Training With Knowledge Graphs Deepai

By switzerlandersing On Sep 13, 2025

CONTRASTIVE LANGUAGE IMAGE PRE-TRAINING | PDF | Data Compression | Statistical Classification

CONTRASTIVE LANGUAGE IMAGE PRE-TRAINING | PDF | Data Compression | Statistical Classification In this paper, we propose a knowledge based pre training framework, dubbed knowledge clip, which injects semantic information into the widely used clip model. Through introducing knowledge based objectives in the pre training process and utilizing different types of knowledge graphs as training data, our model can semantically align the representations in vision and language with higher quality, and enhance the reasoning ability across scenarios and modalities.

Democratizing Contrastive Language-Image Pre-training: A CLIP Benchmark Of Data, Model, And ...

Democratizing Contrastive Language-Image Pre-training: A CLIP Benchmark Of Data, Model, And ... In this paper, we propose a knowledge based pre training framework, dubbed knowledge clip, which injects semantic information into the widely used clip model. In this article, we introduce a simple yet effective knowledge enhanced model, college (co ntrastive l anguage know le dge g raph pr e training), which leverages contrastive learning to incorporate factual knowledge into plms. We demonstrate that the simple e training task of predicting which caption goes with which image is an efficient and scalable way to learn sota image representations from scratch on a dataset of 400 million (image, text) pairs collected from the internet. Recent years have witnessed the fast development of large scale pre training frameworks that can extract multi modal representations in a unified form and achieve promising performances when transferred to downstream tasks.

Contrastive Language-Image Pre-training (CLIP) - Metaphysic.ai

Contrastive Language-Image Pre-training (CLIP) - Metaphysic.ai We demonstrate that the simple e training task of predicting which caption goes with which image is an efficient and scalable way to learn sota image representations from scratch on a dataset of 400 million (image, text) pairs collected from the internet. Recent years have witnessed the fast development of large scale pre training frameworks that can extract multi modal representations in a unified form and achieve promising performances when transferred to downstream tasks. Contrastive language image pre training (clip) models excel in integrating semantic information between images and text through contrastive learning techniques. it has achieved remarkable performance in various multimodal tasks. We introduce prototypical contrastive language image pretraining (protoclip) to enhance such grouping by boosting its efficiency and increasing its robustness against modality gap. This approach maintains the knowledge in its original graph structure to provide the most available information and circumvents the issue of heterogeneous embedding fusion. Figure 1: comparison between clip and knowledge clip with opposite semantic descriptions, e.g., adding ’not’ in the template or describing an image with wrong color.

Figure 2 From Contrastive Language-Image Pre-Training With Knowledge Graphs | Semantic Scholar

Figure 2 From Contrastive Language-Image Pre-Training With Knowledge Graphs | Semantic Scholar Contrastive language image pre training (clip) models excel in integrating semantic information between images and text through contrastive learning techniques. it has achieved remarkable performance in various multimodal tasks. We introduce prototypical contrastive language image pretraining (protoclip) to enhance such grouping by boosting its efficiency and increasing its robustness against modality gap. This approach maintains the knowledge in its original graph structure to provide the most available information and circumvents the issue of heterogeneous embedding fusion. Figure 1: comparison between clip and knowledge clip with opposite semantic descriptions, e.g., adding ’not’ in the template or describing an image with wrong color.

Understand CLIP (Contrastive Language-Image Pre-Training) — Visual Models From NLP – Studytrails

Understand CLIP (Contrastive Language-Image Pre-Training) — Visual Models From NLP – Studytrails This approach maintains the knowledge in its original graph structure to provide the most available information and circumvents the issue of heterogeneous embedding fusion. Figure 1: comparison between clip and knowledge clip with opposite semantic descriptions, e.g., adding ’not’ in the template or describing an image with wrong color.