Video anomaly detection deals with the recognition of abnormal events in
videos. Apart from the visual signal, video anomaly detection has also been
addressed with the use of skeleton sequences. We propose a holistic
representation of skeleton trajectories to learn expected motions across
segments at different times. Our approach uses multitask learning to
reconstruct any continuous unobserved temporal segment of the trajectory
allowing the extrapolation of past or future segments and the interpolation of
in-between segments. We use an end-to-end attention-based encoder-decoder. We
encode temporally occluded trajectories, jointly learn latent representations
of the occluded segments, and reconstruct trajectories based on expected
motions across different temporal segments. Extensive experiments on three
trajectory-based video anomaly detection datasets show the advantages and
effectiveness of our approach with state-of-the-art results on anomaly
detection in skeleton trajectories.

视频异常检测中，除了视觉信号外，我们还应用了骨架序列。我们提出了一种完整的骨架轨迹表示方法，以学习不同时间段内预期的动作。我们使用多任务学习来重建轨迹的任何未观测连续时间段，从而实现过去或未来时间段的外推和插值。我们采用了端到端的基于注意力的编码器 - 解码器模型，并对轨迹进行编码，联合学习被时间遮挡的轨迹段的潜在表示，并根据不同时间段内的预期动作进行重建。在三个基于轨迹的视频异常检测数据集上进行的大量实验证明了我们方法的优势和有效性，并在骨架轨迹的异常检测中取得了最先进的结果。

多任务轨迹异常检测的整体表示学习

Holistic Representation Learning for Multitask Trajectory Anomaly  Detection

Reading irregular scene text of arbitrary shape in natural images is still a
challenging problem, despite the progress made recently. Many existing
approaches incorporate sophisticated network structures to handle various
shapes, use extra annotations for stronger supervision, or employ hard-to-train
recurrent neural networks for sequence modeling. In this work, we propose a
simple yet strong approach for scene text recognition. With no need to convert
input images to sequence representations, we directly connect two-dimensional
CNN features to an attention-based sequence decoder which guided by holistic
representation. The holistic representation can guide the attention-based
decoder focus on more accurate area. As no recurrent module is adopted, our
model can be trained in parallel. It achieves 1.5x to 9.4x acceleration to
backward pass and 1.3x to 7.9x acceleration to forward pass, compared with the
RNN counterparts. The proposed model is trained with only word-level
annotations. With this simple design, our method achieves state-of-the-art or
competitive recognition performance on the evaluated regular and irregular
scene text benchmark datasets.

本研究提出一种基于全局特征引导注意力的二维卷积神经网络和序列解码器来识别场景文本的简单但强大方法，训练时仅使用单词级别的注释，相比循环神经网络，能够在前向和后向传递中获得 1.3x 至 9.4x 不等的加速。在评估的正常和不规则的场景文本基准数据集上实现了最先进或竞争性的识别性能。