Cross-modal de-deviation for enhancing few-shot classification

Meihong Pan, Hong-Bin Shen

Abstract: Highlights•Our CMDD method reduces prototype deviation through cross-modal label assignment, mitigating the risk of collapsing multiple clusters into one class and minimizing the impact of limited labeled samples on the refined prototypes.•An alternative optimization strategy based on the alternating least squares model is explored to optimize the features and classifier’s weights, effectively promoting mutual enhancement between them.•Our CMDD method competes well with state-of-the-art approaches, demonstrated through comprehensive experiments and ablation studies on four few-shot benchmarks.

External IDs:dblp:journals/pr/PanS24