HyFI: Hyperbolic Feature Interpolation for Brain-Vision Alignment
arXiv:2603.22721v1 Announce Type: new Abstract: Recent progress in artificial intelligence has encouraged numerous attempts to understand and decode human visual system from brain signals. These prior works typically align neural activity independently with semantic and perceptual features extracted from images using pre-trained vision models. However, they fail to account for two key challenges: (1) the modality gap arising from the natural difference in the information level of representation between brain signals and images, and (2) the fact that semantic and perceptual features are highly entangled within neural activity. To address these issues, we utilize hyperbolic space, which is well-suited for considering differences in the amount of information and has the geometric property that geodesics between two points naturally bend toward the origin, where the representational capacity is lower. Leveraging these properties, we propose a novel framework, Hyperbolic Feature Interpolat
arXiv:2603.22721v1 Announce Type: new Abstract: Recent progress in artificial intelligence has encouraged numerous attempts to understand and decode human visual system from brain signals. These prior works typically align neural activity independently with semantic and perceptual features extracted from images using pre-trained vision models. However, they fail to account for two key challenges: (1) the modality gap arising from the natural difference in the information level of representation between brain signals and images, and (2) the fact that semantic and perceptual features are highly entangled within neural activity. To address these issues, we utilize hyperbolic space, which is well-suited for considering differences in the amount of information and has the geometric property that geodesics between two points naturally bend toward the origin, where the representational capacity is lower. Leveraging these properties, we propose a novel framework, Hyperbolic Feature Interpolation (HyFI), which interpolates between semantic and perceptual visual features along hyperbolic geodesics. This enables both the fusion and compression of perceptual and semantic information, effectively reflecting the limited expressiveness of brain signals and the entangled nature of these features. As a result, it facilitates better alignment between brain and visual features. We demonstrate that HyFI achieves state-of-the-art performance in zero-shot brain-to-image retrieval, outperforming prior methods with Top-1 accuracy improvements of up to +17.3% on THINGS-EEG and +9.1% on THINGS-MEG.
Executive Summary
The article presents a novel framework, Hyperbolic Feature Interpolation (HyFI), which addresses the modality gap and entanglement of semantic and perceptual features in brain-vision alignment. By leveraging hyperbolic space, HyFI interpolates between visual features, facilitating better alignment between brain and visual features. The framework achieves state-of-the-art performance in zero-shot brain-to-image retrieval, outperforming prior methods with significant accuracy improvements.
Key Points
- ▸ HyFI utilizes hyperbolic space to address modality gap and feature entanglement
- ▸ The framework interpolates between semantic and perceptual visual features along hyperbolic geodesics
- ▸ HyFI achieves state-of-the-art performance in zero-shot brain-to-image retrieval
Merits
Effective Feature Alignment
HyFI's ability to interpolate between semantic and perceptual features enables effective alignment between brain and visual features, leading to improved performance in brain-to-image retrieval tasks.
Demerits
Complexity of Hyperbolic Space
The use of hyperbolic space may introduce additional complexity, requiring specialized knowledge and computational resources, which could limit the framework's adoption and scalability.
Expert Commentary
The introduction of HyFI marks a significant advancement in brain-vision alignment, addressing long-standing challenges in the field. By leveraging the unique properties of hyperbolic space, the framework provides a more effective and efficient means of aligning brain and visual features. However, further research is needed to fully explore the potential of HyFI and its applications in various fields, including neuroscience and computer vision. The framework's ability to facilitate better understanding of brain activity and its relationship to visual perception has far-reaching implications for both theoretical and practical applications.
Recommendations
- ✓ Further investigation into the applications of HyFI in neural decoding and brain-computer interfaces
- ✓ Development of more efficient and scalable implementations of HyFI, enabling wider adoption and use in various fields
Sources
Original: arXiv - cs.AI
