Group-level emotion recognition (GER) is an inseparable part of human
behavior analysis, aiming to recognize an overall emotion in a multi-person
scene. However, the existing methods are devoted to combing diverse emotion
cues while ignoring the inherent uncertainties under unconstrained
environments, such as congestion and occlusion occurring within a group.
Additionally, since only group-level labels are available, inconsistent emotion
predictions among individuals in one group can confuse the network. In this
paper, we propose an uncertainty-aware learning (UAL) method to extract more
robust representations for GER. By explicitly modeling the uncertainty of each
individual, we utilize stochastic embedding drawn from a Gaussian distribution
instead of deterministic point embedding. This representation captures the
probabilities of different emotions and generates diverse predictions through
this stochasticity during the inference stage. Furthermore,
uncertainty-sensitive scores are adaptively assigned as the fusion weights of
individuals' face within each group. Moreover, we develop an image enhancement
module to enhance the model's robustness against severe noise. The overall
three-branch model, encompassing face, object, and scene component, is guided
by a proportional-weighted fusion strategy and integrates the proposed
uncertainty-aware method to produce the final group-level output. Experimental
results demonstrate the effectiveness and generalization ability of our method
across three widely used databases.

我们提出了一种考虑不确定性的学习方法，通过明确建模每个个体的不确定性，利用从高斯分布中绘制的随机嵌入，而不是确定性点嵌入，来提取更稳健的组级情绪表示。该方法通过随机性在推理阶段生成不同情绪的概率，从而产生多样化的预测。此外，我们根据不确定性敏感分数自适应地分配个体脸部的融合权重，并开发了一个图像增强模块，以增强模型对严重噪声的鲁棒性。我们的三分支模型包含脸部、物体和场景组件，通过比例加权融合策略和集成所提出的不确定性感知方法来生成最终的组级输出，实验结果证明了我们的方法在三个广泛使用的数据库上的有效性和泛化能力。