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Fair Comparison: Quantifying Variance in Resultsfor Fine-grained Visual Categorization
From this analysis, we both highlight the importance of reporting and comparing methods based on information beyond overall accuracy, as well as point out techniques that mitigate variance in FGVC results.
Facing the Hard Problems in FGVC
In fine-grained visual categorization (FGVC), there is a near-singular focus in pursuit of attaining state-of-the-art (SOTA) accuracy.
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