Reliability-Controlled Competitive Assignment for Conflict-Free Pseudo-Labeling in Few-Shot Multimodal Learning

Prototype-guided self-training expands a small labeled set by assigning unlabeled instances to class prototypes. Under few-shot supervision, however, overlapping class-specific candidate sets create an assignment ambiguity: independent per-class selection can admit the same instance with conflicting pseudo-labels. Confidence thresholds and class-aware admission rules regulate sample selection but do not, by themselves, resolve competition between classes. We propose **reliability-controlled competitive assignment**, which scores candidate assignments using a dual-constraint reliability criterion and resolves cross-class conflicts through a per-instance argmin before admission. This enforces a one-instance–one-label constraint, guaranteeing an admitted pool free of duplicate-instance label conflicts without assuming that every assigned label is correct. We integrate competitive assignment into a progressive self-training framework that combines class-balanced, confidence-guided pool expansion with momentum-smoothed pseudo-label refinement. Experiments on two few-shot multimodal classification benchmarks, **MUUFL Gulfport** and **Trento**, compare competitive assignment with independent per-class selection using matched-seed paired runs. Competitive assignment improves mean accuracy by **7.95 percentage points on MUUFL** (95% confidence interval: **[3.15, 12.75]**) and **4.79 percentage points on Trento**, although uncertainty is greater in Trento’s five-split evaluation. These findings demonstrate that resolving cross-class assignment conflicts before admission can improve few-shot multimodal classification and establish assignment consistency as a useful complement to confidence-based pseudo-label selection.