CLIP vs SigLIP vs AIM:

Author(s): Nahid Alam

Originally published on Towards AI.

Understanding Image Encoders for Multimodal LLMs

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Image encoders serve a critical role in representing raw images in a form that computers understand. For an AI system, this means a vector representation of image pixels. In traditional computer vision tasks such as classification, object detection, and segmentation, encoders like Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) learn representations that capture spatial and semantic information, enabling downstream models to identify patterns and make predictions.

In this note, I am interested in understanding image encoders in the context of Multimodal LLMs such as image-text models. In multimodal models, image encoders are tasked with bridging visual data with other modalities, such as text. This means that the image encoders create vector representation of an image in a way that it aligns with the vector representation of the text. How well this alignment happens typically indicates the quality of these encoders.

You may ask — why is the quality of these image encoders important? And specifically, why do we care? If you are building LLaVA like multimodal vision-language models as shown in Figure 1 below, you will notice that vision encoders are a key component here. Performance of vision encoders matters… Read the full blog for free on Medium.

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Published via Towards AI