Gait recognition holds the promise of robustly identifying subjects based on
their walking patterns instead of color information. While previous approaches
have performed well for curated indoor scenes, they have significantly impeded
applicability in unconstrained situations, e.g. outdoor, long distance scenes.
We propose an end-to-end GAit DEtection and Recognition (GADER) algorithm for
human authentication in challenging outdoor scenarios. Specifically, GADER
leverages a Double Helical Signature to detect the fragment of human movement
and incorporates a novel gait recognition method, which learns representations
by distilling from an auxiliary RGB recognition model. At inference time, GADER
only uses the silhouette modality but benefits from a more robust
representation. Extensive experiments on indoor and outdoor datasets
demonstrate that the proposed method outperforms the State-of-The-Arts for gait
recognition and verification, with a significant 20.6% improvement on
unconstrained, long distance scenes.

我们提出了一种用于挑战性户外场景中的人体验证的端到端步态检测和识别（GADER）算法，该算法利用双螺旋签名来检测人体动作的片段，并结合一种新颖的步态识别方法，在辅助 RGB 识别模型的基础上学习表示。在推理时，GADER 仅使用轮廓模态，并从中获得更强大的表示能力。对室内和室外数据集进行的广泛实验表明，该方法在步态识别和验证方面优于现有方法，在非受限的远距离场景中有显著的 20.6% 改进。

GADER: 野外行为识别与识别

GADER: GAit DEtection and Recognition in the Wild

Vision and language navigation (VLN) is a challenging visually-grounded
language understanding task. Given a natural language navigation instruction, a
visual agent interacts with a graph-based environment equipped with panorama
images and tries to follow the described route. Most prior work has been
conducted in indoor scenarios where best results were obtained for navigation
on routes that are similar to the training routes, with sharp drops in
performance when testing on unseen environments. We focus on VLN in outdoor
scenarios and find that in contrast to indoor VLN, most of the gain in outdoor
VLN on unseen data is due to features like junction type embedding or heading
delta that are specific to the respective environment graph, while image
information plays a very minor role in generalizing VLN to unseen outdoor
areas. These findings show a bias to specifics of graph representations of
urban environments, demanding that VLN tasks grow in scale and diversity of
geographical environments.

本研究探讨了在室外场景中，视觉代理如何跨越未知地形实现指令导航，发现与室内情况相比，图形化表达方式对室外导航具有更大的影响，因此未来的研究需关注在不同地形环境中的规模和多样性增长。