  As an instance-level recognition problem, re-identification (re-ID) requires
models to capture diverse features. However, with continuous training, re-ID
models pay more and more attention to the salient areas. As a result, the model
may only focus on few small regions with salient representations and ignore
other important information. This phenomenon leads to inferior performance,
especially when models are evaluated on small inter-identity variation data. In
this paper, we propose a novel network, Erasing-Salient Net (ES-Net), to learn
comprehensive features by erasing the salient areas in an image. ES-Net
proposes a novel method to locate the salient areas by the confidence of
objects and erases them efficiently in a training batch. Meanwhile, to mitigate
the over-erasing problem, this paper uses a trainable pooling layer P-pooling
that generalizes global max and global average pooling. Experiments are
conducted on two specific re-identification tasks (i.e., Person re-ID, Vehicle
re-ID). Our ES-Net outperforms state-of-the-art methods on three Person re-ID
benchmarks and two Vehicle re-ID benchmarks. Specifically, mAP / Rank-1 rate:
88.6% / 95.7% on Market1501, 78.8% / 89.2% on DuckMTMC-reID, 57.3% / 80.9% on
MSMT17, 81.9% / 97.0% on Veri-776, respectively. Rank-1 / Rank-5 rate: 83.6% /
96.9% on VehicleID (Small), 79.9% / 93.5% on VehicleID (Medium), 76.9% / 90.7%
on VehicleID (Large), respectively. Moreover, the visualized salient areas show
human-interpretable visual explanations for the ranking results.
