Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Enhancing CLIP Conceptual Embedding through Knowledge Distillation

Kuei-Chun Kao

TL;DR

This work presents Knowledge-CLIP, a framework to augment CLIP by incorporating knowledge from the large language model Llama 2 through knowledge distillation, soft concept labeling, and contrastive learning. It introduces Text Embedding Distillation to align CLIP's text encoder with Llama 2, Concept Learning to impose soft concept labels via K-means on Llama 2 embeddings, and a standard CLIP-like Contrastive Objective to maintain cross-modal alignment. The approach yields measurable gains in both text- and image-encoder quality: the text evaluation shows Knowledge-CLIP's EM on CC3M lying between Llama 2 and CLIP, while the image evaluation on AWA2 and CUB shows modest improvements over CLIP. These results indicate that external knowledge can enrich multimodal embeddings and improve downstream understanding, with room for broader evaluation and hyperparameter tuning in future work.

Abstract

Recently, CLIP has become an important model for aligning images and text in multi-modal contexts. However, researchers have identified limitations in the ability of CLIP's text and image encoders to extract detailed knowledge from pairs of captions and images. In response, this paper presents Knowledge-CLIP, an innovative approach designed to improve CLIP's performance by integrating a new knowledge distillation (KD) method based on Llama 2. Our approach focuses on three key objectives: Text Embedding Distillation, Concept Learning, and Contrastive Learning. First, Text Embedding Distillation involves training the Knowledge-CLIP text encoder to mirror the teacher model, Llama 2. Next, Concept Learning assigns a soft concept label to each caption-image pair by employing offline K-means clustering on text data from Llama 2, enabling Knowledge-CLIP to learn from these soft concept labels. Lastly, Contrastive Learning aligns the text and image embeddings. Our experimental findings show that the proposed model improves the performance of both text and image encoders.

Enhancing CLIP Conceptual Embedding through Knowledge Distillation

TL;DR

This work presents Knowledge-CLIP, a framework to augment CLIP by incorporating knowledge from the large language model Llama 2 through knowledge distillation, soft concept labeling, and contrastive learning. It introduces Text Embedding Distillation to align CLIP's text encoder with Llama 2, Concept Learning to impose soft concept labels via K-means on Llama 2 embeddings, and a standard CLIP-like Contrastive Objective to maintain cross-modal alignment. The approach yields measurable gains in both text- and image-encoder quality: the text evaluation shows Knowledge-CLIP's EM on CC3M lying between Llama 2 and CLIP, while the image evaluation on AWA2 and CUB shows modest improvements over CLIP. These results indicate that external knowledge can enrich multimodal embeddings and improve downstream understanding, with room for broader evaluation and hyperparameter tuning in future work.

Abstract

Recently, CLIP has become an important model for aligning images and text in multi-modal contexts. However, researchers have identified limitations in the ability of CLIP's text and image encoders to extract detailed knowledge from pairs of captions and images. In response, this paper presents Knowledge-CLIP, an innovative approach designed to improve CLIP's performance by integrating a new knowledge distillation (KD) method based on Llama 2. Our approach focuses on three key objectives: Text Embedding Distillation, Concept Learning, and Contrastive Learning. First, Text Embedding Distillation involves training the Knowledge-CLIP text encoder to mirror the teacher model, Llama 2. Next, Concept Learning assigns a soft concept label to each caption-image pair by employing offline K-means clustering on text data from Llama 2, enabling Knowledge-CLIP to learn from these soft concept labels. Lastly, Contrastive Learning aligns the text and image embeddings. Our experimental findings show that the proposed model improves the performance of both text and image encoders.

Paper Structure

This paper contains 18 sections, 3 equations, 5 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Works
  3. Knowledge Distillation
  4. CLIP's Text Encoder
  5. Method
  6. Problem Definition and Annotations
  7. Text Embedding Distillation
  8. Concept Learning
  9. Contrastive Learning
  10. Learning Objective
  11. Experiment
  12. Experimental settings
  13. Knowledge-CLIP's Text Encoder
  14. Knowledge-CLIP's Image Encoder
  15. Discussion
  16. ...and 3 more sections

Figures (5)

  • Figure 1: Overview of our proposed Knowledge-CLIP, which has five modules: CLIP text encoder ($E_{T}$), CLIP image encoder ($E_{I}$), Classifier ($C$), Linear projector ($W_e$) and Llama 2 ($L$).
  • Figure 2: Numpy-like pseudocode for the core of an implementation of CLIP.
  • Figure 3: The evaluation process of text encoders
  • Figure 4: The distribution of text embedding generated by Llama 2 and CLIP
  • Figure 5: Visualization of Llama 2's embeddings with different soft concept labels. Colors represent different attributes in CUB Wah2011TheCB dataset.