  Granularity and accuracy are two crucial factors for crime event prediction.
Within fine-grained event classification, multiple criminal intents may
alternately exhibit in preceding sequential events, and progress differently in
next. Such intensive intent dynamics makes training models hard to capture
unobserved intents, and thus leads to sub-optimal generalization performance,
especially in the intertwining of numerous potential events. To capture
comprehensive criminal intents, this paper proposes a fine-grained sequential
crime prediction framework, CrimeAlarm, that equips with a novel mutual
distillation strategy inspired by curriculum learning. During the early
training phase, spot-shared criminal intents are captured through
high-confidence sequence samples. In the later phase, spot-specific intents are
gradually learned by increasing the contribution of low-confidence sequences.
Meanwhile, the output probability distributions are reciprocally learned
between prediction networks to model unobserved criminal intents. Extensive
experiments show that CrimeAlarm outperforms state-of-the-art methods in terms
of NDCG@5, with improvements of 4.51% for the NYC16 and 7.73% for the CHI18 in
accuracy measures.
