Appau Gideon Kofi Amo

Appau Gideon Kofi Amo

M.S. student at USTC working on optimizer-level methods for long-tailed visual recognition and on world models for decision-making.

Publications

Manuscripts

Sharpness-Feedback Classwise Allocation for Long-Tailed Visual Recognition

Published in Manuscript in preparation (target: CVPR), 2026

SFCA combines a static log-scarcity prior with an online per-class SAM ascent-gap signal (robustly normalized EMA plus a bounded sigmoid mapping) to allocate per-class sharpness during training. On CIFAR-100-LT (IR=200, Logit Adjustment) it raises tail accuracy from 22.45% to 24.49% with zero inference overhead.

Recommended citation: Appau, Gideon Kofi Amo, and Wang, Pengkun. (2026). "Sharpness-Feedback Classwise Allocation for Long-Tailed Visual Recognition." Manuscript in preparation.
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Class-Adaptive Focal Sharpness-Aware Minimization for Long-Tailed Visual Recognition

Published in Manuscript in preparation (target: CVPR), 2026

CA-Focal-SAM redistributes sharpness-aware minimization at the optimizer level: a frequency-conditioned GammaAdaptor assigns per-class focal parameters, improving tail-class accuracy by 6.03% on CIFAR-10-LT (IR=200) with no architectural change and zero inference overhead.

Recommended citation: Appau, Gideon Kofi Amo, and Wang, Pengkun. (2026). "Class-Adaptive Focal Sharpness-Aware Minimization for Long-Tailed Visual Recognition." Manuscript in preparation.
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