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Despite great strides made on fine-grained visual classification (FGVC),current methods are still heavily reliant on fully-supervised paradigms whereample expert labels are called for. Semi-supervised learning (SSL) techniques,acquiring knowledge from unlabeled data, provide a considerable means forwardand have shown great promise for coarse-grained problems. However, exiting SSLparadigms mostly assume in-distribution (i.e., category-aligned) unlabeleddata, which hinders their effectiveness when re-proposed on FGVC. In thispaper, we put forward a novel design specifically aimed at makingout-of-distribution data work for semi-supervised FGVC, i.e., to "clue themin". We work off an important assumption that all fine-grained categoriesnaturally follow a hierarchical structure (e.g., the phylogenetic tree of"Aves" that covers all bird species). It follows that, instead of operating onindividual samples, we can instead predict sample relations within this treestructure as the optimization goal of SSL. Beyond this, we further introducedtwo strategies uniquely brought by these tree structures to achieveinter-sample consistency regularization and reliable pseudo-relation. Ourexperimental results reveal that (i) the proposed method yields good robustnessagainst out-of-distribution data, and (ii) it can be equipped with prior arts,boosting their performance thus yielding state-of-the-art results. Code isavailable at https://github.com/PRIS-CV/RelMatch.

Clue Me In: Semi-Supervised FGVC with Out-of-Distribution Data