Gait datasets are essential for gait research. However, this paper observes
that present benchmarks, whether conventional constrained or emerging
real-world datasets, fall short regarding covariate diversity. To bridge this
gap, we undertake an arduous 20-month effort to collect a cross-covariate gait
recognition (CCGR) dataset. The CCGR dataset has 970 subjects and about 1.6
million sequences; almost every subject has 33 views and 53 different
covariates. Compared to existing datasets, CCGR has both population and
individual-level diversity. In addition, the views and covariates are well
labeled, enabling the analysis of the effects of different factors. CCGR
provides multiple types of gait data, including RGB, parsing, silhouette, and
pose, offering researchers a comprehensive resource for exploration. In order
to delve deeper into addressing cross-covariate gait recognition, we propose
parsing-based gait recognition (ParsingGait) by utilizing the newly proposed
parsing data. We have conducted extensive experiments. Our main results show:
1) Cross-covariate emerges as a pivotal challenge for practical applications of
gait recognition. 2) ParsingGait demonstrates remarkable potential for further
advancement. 3) Alarmingly, existing SOTA methods achieve less than 43%
accuracy on the CCGR, highlighting the urgency of exploring cross-covariate
gait recognition. Link: this https URL

本文描述了一个 20 个月的工作，收集了一个具有交叉共变量的步态识别数据集，提出了基于解析数据的步态识别方法 ParsingGait，通过广泛的实验证明了交叉共变量对步态识别的挑战，并揭示了现有方法在此数据集上的精度低于 43% 的紧迫性。

跨协变量步态识别：基准测试

Cross-Covariate Gait Recognition: A Benchmark

The contextual bandit literature has traditionally focused on algorithms that
address the exploration-exploitation tradeoff. In particular, greedy algorithms
that exploit current estimates without any exploration may be sub-optimal in
general. However, exploration-free greedy algorithms are desirable in practical
settings where exploration may be costly or unethical (e.g., clinical trials).
Surprisingly, we find that a simple greedy algorithm can be rate optimal
(achieves asymptotically optimal regret) if there is sufficient randomness in
the observed contexts (covariates). We prove that this is always the case for a
two-armed bandit under a general class of context distributions that satisfy a
condition we term covariate diversity. Furthermore, even absent this condition,
we show that a greedy algorithm can be rate optimal with positive probability.
Thus, standard bandit algorithms may unnecessarily explore. Motivated by these
results, we introduce Greedy-First, a new algorithm that uses only observed
contexts and rewards to determine whether to follow a greedy algorithm or to
explore. We prove that this algorithm is rate optimal without any additional
assumptions on the context distribution or the number of arms. Extensive
simulations demonstrate that Greedy-First successfully reduces exploration and
outperforms existing (exploration-based) contextual bandit algorithms such as
Thompson sampling or upper confidence bound (UCB).

证明了当观察到的上下文具有足够的随机性时，贪心算法可以达到速率最优，并介绍了一种新的算法 Greedy-First，该算法仅使用观察到的上下文和奖励来确定是否按照贪心策略或探索策略，同时证明该算法在没有对上下文分布或手臂数量做任何其他假设的情况下具有速率最优的性质，并大量模拟结果表明，Greedy-First 成功地减少了探索并优于现有的基于 Exploration 的上下文 bandit 算法，如 Thompson sampling 或 upper confidence bound（UCB）。