SpikeCLIP: A contrastive language-image pretrained spiking neural network

Area of Science:

• Artificial Intelligence
• Computational Neuroscience
• Machine Learning

Background:

• Spiking Neural Networks (SNNs) show promise for energy-efficient AI, comparable to Artificial Neural Networks (ANNs).
• Integrating linguistic and visual data in SNNs for multimodal tasks is challenging and underexplored.
• Existing SNN research has not fully addressed multimodal learning scenarios.

Purpose of the Study:

• To introduce SpikeCLIP, a novel framework for bridging the modality gap in spike-based multimodal computation.
• To enable SNNs to effectively process and integrate both visual and linguistic information.
• To advance energy-efficient and biologically plausible AI systems for multimodal tasks.

Main Methods:

• Developed a two-step approach: alignment pre-training and dual-loss fine-tuning.
• Utilized spike trains for unified representation of linguistic and visual features.
• Evaluated the framework on standard multimodal datasets.

Main Results:

• SpikeCLIP achieves performance comparable to ANNs in multimodal tasks.
• Demonstrated substantial energy consumption reduction using SNNs compared to ANNs.
• Showcased robust image classification, including out-of-category examples.

Conclusions:

• SNNs can effectively handle multimodal learning with competitive performance and significant energy savings.
• SpikeCLIP offers a viable solution for energy-efficient, biologically plausible multimodal AI.
• This work opens new avenues for SNNs in complex, real-world AI applications.