GNSP: Gradient Null Space Projection for Preserving Cross-Modal Alignment in VLMs Continual Learning

• URL: http://arxiv.org/abs/2507.19839v1
• Date: Sat, 26 Jul 2025 07:22:12 GMT
• Title: GNSP: Gradient Null Space Projection for Preserving Cross-Modal Alignment in VLMs Continual Learning
• Authors: Tiantian Peng, Yuyang Liu, Shuo Yang, Qiuhe Hong, YongHong Tian,
• Abstract summary: Contrastive Language-Image Pretraining has demonstrated remarkable zero-shot generalization by aligning visual and textual modalities in a shared embedding space.<n>When continuously fine-tuned on diverse tasks, CLIP suffers from catastrophic forgetting and degradation of its embedding alignment.<n>We propose Gradient Null Space Projection (GNSP), an efficient continual learning method that projects task-specific gradients onto the null space of previously learned knowledge.
• Score: 27.9960664846484
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Contrastive Language-Image Pretraining has demonstrated remarkable zero-shot generalization by aligning visual and textual modalities in a shared embedding space. However, when continuously fine-tuned on diverse tasks, CLIP suffers from catastrophic forgetting and degradation of its embedding alignment, undermining its zero-shot capabilities. In this work, we propose Gradient Null Space Projection (GNSP), an efficient continual learning method that projects task-specific gradients onto the null space of previously learned knowledge. This orthogonal projection mathematically prevents interference with previous tasks without relying on rehearsal or architectural modification. Furthermore, to preserve the inherent generalization property of CLIP, we introduce knowledge distillation and combine it with a modality alignment preservation loss inspired by CLIP pre-training to stabilize the structure of the multimodal embedding space during fine-tuning. On the MTIL benchmark consisting of 11 tasks, our method achieved SOTA performance on both the Average and Last key metrics. More importantly, experiments show that our method successfully maintains the original modality gap and cross-modal retrieval performance of CLIP, confirming its effectiveness in maintaining a robust visual-language space throughout the continual learning process.

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