Avinash Madasu is an AI researcher on the Cognitive AI team at Intel Labs. His work focuses on developing large scale multi-modal models that are fair and interpretable to use in real world applications. Co-author Phillip Howard is an AI research scientist at Intel Labs focused on multimodal AI systems.
Highlights
- CLIP-InterpreT offers a suite of five interpretability analyses to understand the inner workings of Contrastive Language-Image Pretraining (CLIP) vision-language models, which is crucial for responsible artificial intelligence (AI) development.
- Moving beyond simple input-output observations, the tool reveals how different parts of the model contribute to visual and textual understanding, including property recognition and image segmentation.
- By enhancing transparency and explainability, CLIP-InterpreT enables researchers and developers to debug, refine, and build trust in AI systems.
As CLIP-like vision-language models become central to tasks ranging from video retrieval to image generation, understanding how the models’ complex internal processes work is crucial for responsible and safe AI deployment. Intel Labs developed CLIP-InterpreT, an interpretability tool enabling users to examine the inner workings of CLIP-like foundational models in an accessible and structured way. The tool moves beyond simple input-output observations by analyzing individual attention heads, revealing how different parts of the model contribute to visual and textual understanding, including property recognition and image segmentation. CLIP-InterpreT offers five types of analyses, including property-based nearest neighbors search, per-head topic segmentation, contrastive segmentation, per-head nearest neighbors of an image, and per-head nearest neighbors of text. By enhancing transparency and explainability, CLIP-InterpreT enables researchers and developers to debug, refine, and build trust in powerful AI systems, ensuring safer, more reliable AI integration in applications such as autonomous navigation.
Despite the popularity of CLIP models, they are often considered “black boxes” due to their complex architectures and vast datasets. This lack of transparency poses a challenge — without understanding why a model makes certain predictions, it’s difficult to ensure its reliability, fairness, and ethical alignment. This problem is especially relevant in high-stakes applications such as autonomous systems or content moderation, where a model’s decisions must be explainable.
A Comprehensive Suite of Interpretability Analyses
CLIP-InterpreT's interpretability analyses reveal insights into CLIP's inner mechanisms. We can observe which parts of the model specialize in certain properties, how it segments images based on text prompts, and how it establishes connections between visual and textual representations. These insights can be used for debugging and improving models by identifying biases and weaknesses in CLIP-like models. By understanding the models’ decision-making process and how they function, we can develop more reliable and trustworthy AI applications.
CLIP-InterpreT uses five interpretability techniques to help users understand CLIP-like models’ internal processes:
1. Property-based nearest neighbors search: This analysis identifies layers and attention heads in the model that focus on specific properties (for example, colors, locations, or animals) by finding similar images from ImageNet based on these characteristics. Using OpenAI’s ChatGPT for in-context labeling, CLIP-InterpreT identifies recurring properties across layers and heads, helping to characterize different model segments.
Figure 1. Top-4 nearest neighbors for "colors" property using ViT-B-32 model (Data comp). In this example, both the input and retrieved images have common orange, black, and green colors.
2. Per-head topic segmentation: By mapping a given text input to a segmentation map projected onto an image, this technique shows how specific attention heads relate to text-defined concepts, revealing how different heads prioritize elements within an image.
Figure 2. Topic segmentation results for Layer 11, Head 3 "environment/weather" head using ViT-B-16 model (LAION-2B). In the first image pair (left), the heatmap (blue) is focused on "flowers" matching the text description "blossoming springtime blooms." In the second image pair (middle), the heatmap (blue) is concentrated on the "tornado" matching the text description. In the last pair, the heatmap (blue) is focused on the "sun" matching the description "hot summer."
3. Contrastive segmentation: In this analysis, the model contrasts two text inputs to see how they affect the visual interpretation of an image. This enables users to see how the model visually differentiates between concepts, highlighting nuanced understandings of text prompts.
Figure 3. Image shows the contrastive segmentation between portions of the image containing "tornado" and "thunderstorm." The model used is ViT-L-14 (LAION-2B).
4. Per-head nearest neighbors of an image: Using intermediate representations from attention heads, this analysis finds images similar to a given input image based on specific properties (for example, color or object similarity), illustrating the model’s focus on certain visual features.
Figure 4. Top-8 nearest neighbors per head and image. The input image is provided on the left, with the head-specific nearest neighbors shown on the right. The model used in these examples is ViT-B-16 pretrained on OpenAI-400M.
5. Per head nearest neighbors of text: This analysis uses different attention heads to find the most relevant images for a text prompt. It reveals the model’s ability to match textual descriptions with visual examples, reflecting its representation of language in visual space.
Figure 5. Nearest neighbors retrieved for the top TextSpan outputs of a given layer and head. The model used is ViT-B-16 pretrained on OpenAI-400M.
From Debugging to Trust
CLIP-InterpreT is a major milestone in AI interpretability, offering a comprehensive approach to understanding and trusting vision-language models. By enabling users to visualize model attention patterns and text-image relationships, CLIP-InterpreT brings transparency to AI’s inner workings, fostering a future where AI models are not only powerful but also explainable and trustworthy.
Our work is guided by the principles of transparency, explainability, and fairness. CLIP-InterpreT directly addresses the transparency and explainability challenges, promoting responsible development and deployment of vision-language models.
Learn more about CLIP-InterpreT through the research paper and GitHub repository.