  The recent success of Transformer has provided a new direction to various
visual understanding tasks, including video-based facial expression recognition
(FER). By modeling visual relations effectively, Transformer has shown its
power for describing complicated patterns. However, Transformer still performs
unsatisfactorily to notice subtle facial expression movements, because the
expression movements of many videos can be too small to extract meaningful
spatial-temporal relations and achieve robust performance. To this end, we
propose to decompose each video into a series of expression snippets, each of
which contains a small number of facial movements, and attempt to augment the
Transformer's ability for modeling intra-snippet and inter-snippet visual
relations, respectively, obtaining the Expression snippet Transformer (EST). In
particular, for intra-snippet modeling, we devise an attention-augmented
snippet feature extractor (AA-SFE) to enhance the encoding of subtle facial
movements of each snippet by gradually attending to more salient information.
In addition, for inter-snippet modeling, we introduce a shuffled snippet order
prediction (SSOP) head and a corresponding loss to improve the modeling of
subtle motion changes across subsequent snippets by training the Transformer to
identify shuffled snippet orders. Extensive experiments on four challenging
datasets (i.e., BU-3DFE, MMI, AFEW, and DFEW) demonstrate that our EST is
superior to other CNN-based methods, obtaining state-of-the-art performance.
