This paper addresses a new problem of weakly-supervised online action
segmentation in instructional videos. We present a framework to segment
streaming videos online at test time using Dynamic Programming and show its
advantages over greedy sliding window approach. We improve our framework by
introducing the Online-Offline Discrepancy Loss (OODL) to encourage the
segmentation results to have a higher temporal consistency. Furthermore, only
during training, we exploit frame-wise correspondence between multiple views as
supervision for training weakly-labeled instructional videos. In particular, we
investigate three different multi-view inference techniques to generate more
accurate frame-wise pseudo ground-truth with no additional annotation cost. We
present results and ablation studies on two benchmark multi-view datasets,
Breakfast and IKEA ASM. Experimental results show efficacy of the proposed
methods both qualitatively and quantitatively in two domains of cooking and
assembly.

本文提出了一种弱监督的在线动作分割框架，采用动态规划方法来在线分割流媒体视频，并通过引入 Online-Offline Discrepancy Loss（OODL）优化分割结果的时间一致性，同时仅在训练期间使用多视角帧级一致性作为弱标注来提高精度及减少标注成本，实验结果在烹饪和组装等两个领域的 Breakfast 和 IKEA ASM 数据集中均得到了验证。

多视角教学视频的弱监督在线动作分割

Weakly-Supervised Online Action Segmentation in Multi-View Instructional  Videos

Knowledge graphs (KGs) are widely used to facilitate relation extraction (RE)
tasks. While most previous RE methods focus on leveraging deterministic KGs,
uncertain KGs, which assign a confidence score for each relation instance, can
provide prior probability distributions of relational facts as valuable
external knowledge for RE models. This paper proposes to exploit uncertain
knowledge to improve relation extraction. Specifically, we introduce ProBase,
an uncertain KG that indicates to what extent a target entity belongs to a
concept, into our RE architecture. We then design a novel multi-view inference
framework to systematically integrate local context and global knowledge across
three views: mention-, entity- and concept-view. The experimental results show
that our model achieves competitive performances on both sentence- and
document-level relation extraction, which verifies the effectiveness of
introducing uncertain knowledge and the multi-view inference framework that we
design.

本文提出了一种基于不确定知识图谱的关系抽取模型，该模型采用多视角推理框架来整合本地上下文和全局知识。实验结果表明，引入不确定知识和多视角推理框架的模型在句子和文档级别关系抽取上都具有竞争性能。