Existing methods for open-set action recognition focus on novelty detection
that assumes video clips show a single action, which is unrealistic in the real
world. We propose a new method for open set action recognition and novelty
detection via MUlti-Label Evidential learning (MULE), that goes beyond previous
novel action detection methods by addressing the more general problems of
single or multiple actors in the same scene, with simultaneous action(s) by any
actor. Our Beta Evidential Neural Network estimates multi-action uncertainty
with Beta densities based on actor-context-object relation representations. An
evidence debiasing constraint is added to the objective function for
optimization to reduce the static bias of video representations, which can
incorrectly correlate predictions and static cues. We develop a learning
algorithm based on a primal-dual average scheme update to optimize the proposed
problem. Theoretical analysis of the optimization algorithm demonstrates the
convergence of the primal solution sequence and bounds for both the loss
function and the debiasing constraint. Uncertainty and belief-based novelty
estimation mechanisms are formulated to detect novel actions. Extensive
experiments on two real-world video datasets show that our proposed approach
achieves promising performance in single/multi-actor, single/multi-action
settings.

提出了一种基于 MUlti-Label Evidential 学习的方法，使用 Beta Evidential 神经网络评估多种不确定性，添加证据去偏执约束来优化模型，并结合不确定性和置信度机制来检测新颖行为，实验表明这一方法在单 / 多演员，单 / 多动作的情况下实现了良好的性能。

基于多标签证据学习的开放集行为识别

Open Set Action Recognition via Multi-Label Evidential Learning

In this paper, we focus on a prediction-based novelty estimation strategy
upon the deep reinforcement learning (DRL) framework, and present a flow-based
intrinsic curiosity module (FICM) to exploit the prediction errors from optical
flow estimation as exploration bonuses. We propose the concept of leveraging
motion features captured between consecutive observations to evaluate the
novelty of observations in an environment. FICM encourages a DRL agent to
explore observations with unfamiliar motion features, and requires only two
consecutive frames to obtain sufficient information when estimating the
novelty. We evaluate our method and compare it with a number of existing
methods on multiple benchmark environments, including Atari games, Super Mario
Bros., and ViZDoom. We demonstrate that FICM is favorable to tasks or
environments featuring moving objects, which allow FICM to utilize the motion
features between consecutive observations. We further ablatively analyze the
encoding efficiency of FICM, and discuss its applicable domains
comprehensively.

本文提出了一种基于深度强化学习框架的预测为主的新颖性估计策略，并介绍了一种基于流的内部好奇心模块 (FICM)，以利用光流估计中的预测误差作为探索奖励来评估环境中的观测新颖性。我们比较了多个基准环境上的现有方法，并证明了 FICM 对于具有移动物体的任务或环境非常有利。