Research map / Vision-language and multimodal / Cross-modal learning
Lyapunov-Stable Adaptive Control for Multimodal Concept Drift
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Proposes LS-OGD, a rule-based adaptive controller that adjusts learning rate and text/image fusion weight in response to detected concept drift in multimodal learning, with Lyapunov-style UUB guarantees and a small experiment on M3A.
Problem
Multimodal learning systems degrade under concept drift, especially modality-specific drift, and existing adaptation lacks stability guarantees and adaptive fusion.
Approach
An online controller tunes the learning rate and a time-varying late-fusion weight alpha(t) from accuracy-drop drift signals, with a quadratic Lyapunov function V=e^2/2 used to argue uniform ultimate boundedness of the error under bounded drift.
Key results
- Experiments on M3A show the controller's drift signal activates during Phase 2 drift and prediction error is contained and driven down (qualitative figures only).
- Fusion weight alpha_t shifts rapidly under severe unilateral image-modality drift, per the authors' figures; no numeric improvement over baselines is reported in the provided text.
- Theory claims uniform ultimate boundedness of error under bounded drift and convergence to zero once drift ceases.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add drift-responsive learning-rate adjustment | LS-OGD · M3A Phase 2 | prediction error stability | Helped |
| add adaptive fusion that down-weights the drifting modality | LS-OGD · M3A Phase 2 | performance under unilateral modality drift | Helped |
| use LS-OGD adaptation under bounded drift | LS-OGD · bounded concept drift | prediction error bound | Helped |
Limitations
- Lyapunov analysis relies on strong axioms (bounded drift, sufficient capacity, Lipschitz-like dynamics) that are assumed rather than verified.
- Error e_t is not formally defined and the proof steps contain unjustified approximations and inconsistencies (e.g., Lemma 2 sign conventions).
- Empirical validation is limited to a single dataset with qualitative figures and no reported quantitative baselines in the provided text.
Experimental setup
Datasets: M3A
Benchmarks: M3A
Metrics: accuracy prediction error
Baselines: fixed late fusion early fusion standard drift detectors
Builds on: Model reference adaptive control ADWIN AdamW
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