Biometric applications, such as person re-identification (ReID), are often
deployed on energy constrained devices. While recent ReID methods prioritize
high retrieval performance, they often come with large computational costs and
high search time, rendering them less practical in real-world settings. In this
work, we propose an input-adaptive network with multiple exit blocks, that can
terminate computation early if the retrieval is straightforward or noisy,
saving a lot of computation. To assess the complexity of the input, we
introduce a temporal-based classifier driven by a new training strategy.
Furthermore, we adopt a binary hash code generation approach instead of relying
on continuous-valued features, which significantly improves the search process
by a factor of 20. To ensure similarity preservation, we utilize a new ranking
regularizer that bridges the gap between continuous and binary features.
Extensive analysis of our proposed method is conducted on three datasets:
Market1501, MSMT17 (Multi-Scene Multi-Time), and the BGC1 (BRIAR Government
Collection). Using our approach, more than 70% of the samples with compact hash
codes exit early on the Market1501 dataset, saving 80% of the networks
computational cost and improving over other hash-based methods by 60%. These
results demonstrate a significant improvement over dynamic networks and
showcase comparable accuracy performance to conventional ReID methods. Code
will be made available.

提出了一种多出口块的输入自适应网络，可以根据检索的简易程度或噪声提前终止计算，以节省计算资源。通过引入基于时间的分类器和二进制编码生成方法，能够更高效地进行检索，同时采用新的排序规范化器以确保相似性保持。在 Market1501、MSMT17 和 BGC1 数据集上进行了广泛分析，结果表明与其他基于哈希的方法相比，能够节省计算资源、保持相似性，并且具备与传统 ReID 方法相当的准确性。