Understanding Cross-Modal Contributions in Continual Vision-Language Models: A Theoretical Perspective

arXiv:2606.14883 · cs.CV, cs.LG · Submitted 2026-06-12 · Read on arXiv

cs.CV, cs.LG

Submitted: 2026-06-12

Updated: 2026-09-04

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Alignment — 25× in this paper · explained in 1 episode
• Continual Learning — 24× in this paper · explained in 2 episodes
• Fine-Tuning — 14× in this paper · explained in 5 episodes
• Catastrophic Forgetting — 11× in this paper · explained in 5 episodes
• Robustness — 10× in this paper · explained in 5 episodes
• Loss Function — 9× in this paper · explained in 2 episodes
• Contribution Balance Factor — 7× in this paper
• Regularization — 7× in this paper · explained in 3 episodes
• Adaptation — 6× in this paper · explained in 1 episode
• Generalization — 6× in this paper · explained in 13 episodes
• Distribution Shift — 5× in this paper · explained in 5 episodes
• Embeddings — 4× in this paper · explained in 2 episodes
• Accuracy — 3× in this paper · explained in 1 episode
• Alignment Scores — 3× in this paper · explained in 1 episode
• Zero-Shot Transfer — 3× in this paper · explained in 3 episodes
• Cosine Similarity — 2× in this paper · explained in 3 episodes
• Frozen model — 2× in this paper · explained in 2 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Benchmark — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Fisher Information — 1× in this paper · explained in 3 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Maximum Concept Matching — 1× in this paper
• Pruning — 1× in this paper · explained in 4 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Scaling Laws — 1× in this paper · explained in 4 episodes
• Vision-Language Models (VLMs) — 1× in this paper · explained in 6 episodes

Sources

• Ask and Remember: A Questions-Only Replay Strategy for Continual Visual Question Answering
• Temp-SCONE: A Novel Out-of-Distribution Detection and Domain Generalization Framework for Wild Data with Temporal Shift
• T-QPM: Enabling Temporal Out-Of-Distribution Detection and Domain Generalization for Vision-Language Models in Open-World
• Detecting Interpretable Subgroup Drifts
• Progressive Neural Networks

Related papers

• Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories
• AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
• Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
• MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
• TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
• A Survey on Efficient Vision-Language-Action Models