  Frame-level micro- and macro-expression spotting methods require
time-consuming frame-by-frame observation during annotation. Meanwhile,
video-level spotting lacks sufficient information about the location and number
of expressions during training, resulting in significantly inferior performance
compared with fully-supervised spotting. To bridge this gap, we propose a
point-level weakly-supervised expression spotting (PWES) framework, where each
expression requires to be annotated with only one random frame (i.e., a point).
To mitigate the issue of sparse label distribution, the prevailing solution is
pseudo-label mining, which, however, introduces new problems: localizing
contextual background snippets results in inaccurate boundaries and discarding
foreground snippets leads to fragmentary predictions. Therefore, we design the
strategies of multi-refined pseudo label generation (MPLG) and
distribution-guided feature contrastive learning (DFCL) to address these
problems. Specifically, MPLG generates more reliable pseudo labels by merging
class-specific probabilities, attention scores, fused features, and point-level
labels. DFCL is utilized to enhance feature similarity for the same categories
and feature variability for different categories while capturing global
representations across the entire datasets. Extensive experiments on the
CAS(ME)^2, CAS(ME)^3, and SAMM-LV datasets demonstrate PWES achieves promising
performance comparable to that of recent fully-supervised methods.
