Panoramic Activity Recognition (PAR) aims to identify multi-granularity
behaviors performed by multiple persons in panoramic scenes, including
individual activities, group activities, and global activities. Previous
methods 1) heavily rely on manually annotated detection boxes in training and
inference, hindering further practical deployment; or 2) directly employ normal
detectors to detect multiple persons with varying size and spatial occlusion in
panoramic scenes, blocking the performance gain of PAR. To this end, we
consider learning a detector adapting varying-size occluded persons, which is
optimized along with the recognition module in the all-in-one framework.
Therefore, we propose a novel Adapt-Focused bi-Propagating Prototype learning
(AdaFPP) framework to jointly recognize individual, group, and global
activities in panoramic activity scenes by learning an adapt-focused detector
and multi-granularity prototypes as the pretext tasks in an end-to-end way.
Specifically, to accommodate the varying sizes and spatial occlusion of
multiple persons in crowed panoramic scenes, we introduce a panoramic
adapt-focuser, achieving the size-adapting detection of individuals by
comprehensively selecting and performing fine-grained detections on
object-dense sub-regions identified through original detections. In addition,
to mitigate information loss due to inaccurate individual localizations, we
introduce a bi-propagation prototyper that promotes closed-loop interaction and
informative consistency across different granularities by facilitating
bidirectional information propagation among the individual, group, and global
levels. Extensive experiments demonstrate the significant performance of AdaFPP
and emphasize its powerful applicability for PAR.

通过学习适应不同尺寸的被遮挡人员的检测器并采用多粒度原型作为假设任务的方式，我们提出了一种名为 AdaFPP 的全新的自适应聚焦双向原型学习框架，用于在全景活动场景中联合识别个体、群体和全局活动。

AdaFPP: 面向适应性活动识别的双向传播原型学习

AdaFPP: Adapt-Focused Bi-Propagating Prototype Learning for Panoramic  Activity Recognition

Panoramic Activity Recognition (PAR) seeks to identify diverse human
activities across different scales, from individual actions to social group and
global activities in crowded panoramic scenes. PAR presents two major
challenges: 1) recognizing the nuanced interactions among numerous individuals
and 2) understanding multi-granular human activities. To address these, we
propose Social Proximity-aware Dual-Path Network (SPDP-Net) based on two key
design principles. First, while previous works often focus on spatial distance
among individuals within an image, we argue to consider the spatio-temporal
proximity. It is crucial for individual relation encoding to correctly
understand social dynamics. Secondly, deviating from existing hierarchical
approaches (individual-to-social-to-global activity), we introduce a dual-path
architecture for multi-granular activity recognition. This architecture
comprises individual-to-global and individual-to-social paths, mutually
reinforcing each other's task with global-local context through multiple
layers. Through extensive experiments, we validate the effectiveness of the
spatio-temporal proximity among individuals and the dual-path architecture in
PAR. Furthermore, SPDP-Net achieves new state-of-the-art performance with
46.5\% of overall F1 score on JRDB-PAR dataset.

全景活动识别 (PAN) 旨在在拥挤的全景场景中识别多样化的人类活动，从个体动作到社交群体和全球活动。为了解决 PAN 面临的两个主要挑战，我们提出了基于社交接近度感知的双通道网络 (SPDP-Net)。该网络基于两个关键设计原则：首先，考虑个体之间的时空接近度，对于正确理解社交动态来说至关重要；其次，引入双路径架构进行多粒度活动识别，其中包括个体到全球和个体到社交的路径，通过多层次通过全局和局部上下文相互强化彼此的任务。通过大量实验证明了个体之间的时空接近度和双通道架构在 PAN 中的有效性。此外，SPDP-Net 在 JRDB-PAN 数据集上取得了 46.5% 的整体 F1 分数，达到了新的最先进的性能水平。