Recently, with the emergence of retrieval requirements for certain individual in the same superclass,
e.g., birds, persons,
cars, fine-grained recognition task has attracted a significant amount of attention from academia and
industry. In
fine-grained recognition scenario, the inter-class differences are quite diverse and subtle, which makes
it challenging to
extract all the discriminative cues. Traditional training mechanism optimizes the overall
discriminativeness of the whole
feature. It may stop early when some feature elements has been trained to distinguish training samples
well, leaving other
elements insufficiently trained for a feature. This would result in a less generalizable feature
extractor that only
captures major discriminative cues and ignores subtle ones. Therefore, there is a need for a training
mechanism that
enforces the discriminativeness of all the elements in the feature to capture more the subtle visual
cues. In this paper, we
propose a Discrimination-Aware Mechanism (DAM) that iteratively identifies insufficiently trained
elements and improves
them. DAM is able to increase the number of well learned elements, which captures more visual cues by
the feature extractor.
In this way, a more informative representation is learned, which brings better generalization
performance. We show that DAM
can be easily applied to both proxy-based and pair-based loss functions, and thus can be used in most
existing fine-grained
recognition paradigms. Comprehensive experiments on CUB-200-2011, Cars196, Market-1501, and MSMT17
datasets demonstrate the
advantages of our DAM based loss over the related state-of-the-art approaches.