Interpreting and Controlling Vision Foundation Models via Text Explanations
(2310.10591)Abstract
Large-scale pre-trained vision foundation models, such as CLIP, have become de facto backbones for various vision tasks. However, due to their black-box nature, understanding the underlying rules behind these models' predictions and controlling model behaviors have remained open challenges. We present a framework for interpreting vision transformer's latent tokens with natural language. Given a latent token, our framework retains its semantic information to the final layer using transformer's local operations and retrieves the closest text for explanation. Our approach enables understanding of model visual reasoning procedure without needing additional model training or data collection. Based on the obtained interpretations, our framework allows for model editing that controls model reasoning behaviors and improves model robustness against biases and spurious correlations.
Overview
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The paper introduces a novel framework for interpreting and controlling vision foundation models like CLIP using text explanations, addressing challenges in model interpretability and control.
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This methodology interprets vision transformers' visual reasoning through natural language, leveraging transformer architecture without necessitating retraining or additional data.
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Empirical results validate the framework's capability to generate accurate text explanations for visual concepts and enable model adjustments for improved reliability and transparency.
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The research highlights the practical and theoretical implications for enhancing AI transparency and controllability, suggesting future avenues for expanding its applicability and refining model editing features.
Interpreting and Controlling Vision Foundation Models via Text Explanations
Background and Motivation
In recent years, the evolution of large-scale pre-trained vision foundation models, such as CLIP, has significantly enhanced machine learning systems' ability to execute a variety of tasks. These advancements not only provide a solid basis for further research but also present challenges in interpreting and controlling these models' behaviors. The interpretability of models is crucial for establishing trust and understanding between users and AI systems, especially in sensitive applications like medical diagnosis, where decisions need to be transparent and justifiable.
Proposed Framework
This paper presents a novel framework that allows for the interpretation of vision transformer’s latent tokens using natural language. The approach harnesses the transformer architecture's intrinsic properties to map latent token embeddings to corresponding language descriptions. A key feature of this methodology is its ability to interpret and edit model behaviors without necessitating additional training or data collection, marking a significant step toward addressing the open challenges in model interpretability and control.
The framework operates by interpreting the visual reasoning of transformers through text explanations, thus revealing the model's reasoning process as a composition of concepts captured by individual latent tokens. Using an open-world vocabulary, the approach generates descriptions directly corresponding to visual concepts learned within the models. An added benefit is that this does not require modifying the model architecture or retraining, making it an efficient solution for interpreting pre-trained models.
Empirical Results and Framework Validation
The paper provides extensive empirical evidence demonstrating that the proposed framework can generate accurate and meaningful text explanations for latent tokens, aligned with ground-truth visual concepts. Visualization of interpretations alongside attention heat-maps further substantiates the capability to unveil the intricate reasoning process of vision transformers, showing how these models assemble various visual elements into coherent whole concepts.
Moreover, based on the interpretations obtained, the framework offers an avenue for model editing, allowing adjustments to the model's reasoning behavior. This facilitates various interventions, such as rectifying typographical attacks, mitigating spurious correlations, and editing specific model responses. Experiments conducted across several datasets, including the VAW and CelebA datasets, confirm the framework's effectiveness in enabling these controls without additional training.
Implications and Future Directions
The implications of this research are profound, both practically and theoretically. On a practical level, it provides a powerful tool for users to interpret and manipulate model behaviors directly, enhancing the transparency, trustworthiness, and controllability of AI systems. Theoretically, it offers insights into the internal mechanisms of vision transformers, shedding light on their visual reasoning processes.
Looking forward, this work opens several avenues for future research. One potential direction could be to explore the scalability of the proposed method across different model architectures or to extend the framework's applicability to other domains beyond vision. Additionally, further refinement of the model editing capabilities could lead to more robust models, resistant to adversarial attacks and free from undesirable biases.
Conclusion
This study introduces a transformative approach to interpreting and controlling vision foundation models through natural language explanations. Its ability to discern and manipulate the reasoning process of such models without additional resources marks a significant advancement in making AI systems more interpretable and controllable. As AI continues to advance, frameworks such as the one proposed here will be crucial for ensuring that these powerful models can be trusted and used responsibly.
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