Unsupervised Video Object Segmentation (VOS) aims at identifying the contours
of primary foreground objects in videos without any prior knowledge. However,
previous methods do not fully use spatial-temporal context and fail to tackle
this challenging task in real-time. This motivates us to develop an efficient
Long-Short Temporal Attention network (termed LSTA) for unsupervised VOS task
from a holistic view. Specifically, LSTA consists of two dominant modules,
i.e., Long Temporal Memory and Short Temporal Attention. The former captures
the long-term global pixel relations of the past frames and the current frame,
which models constantly present objects by encoding appearance pattern.
Meanwhile, the latter reveals the short-term local pixel relations of one
nearby frame and the current frame, which models moving objects by encoding
motion pattern. To speedup the inference, the efficient projection and the
locality-based sliding window are adopted to achieve nearly linear time
complexity for the two light modules, respectively. Extensive empirical studies
on several benchmarks have demonstrated promising performances of the proposed
method with high efficiency.

无监督视频对象分割（VOS）旨在识别视频中主要前景对象的轮廓，但以往的方法没有充分利用时空上下文，并且无法在实时中处理这一具有挑战性的任务。因此，本文提出了一种高效的长短时序注意力网络（LSTA），从整体视角解决了无监督 VOS 任务。该网络由长时序记忆和短时序注意力两个主要模块组成，前者通过编码外观模式来捕获过去帧和当前帧的长期全局像素关系，后者通过编码动态模式来揭示附近帧和当前帧的短期局部像素关系。为了加快推理速度，采用了高效投影和基于局部性的滑动窗口，分别实现了两个轻量级模块的几乎线性时间复杂度。在几个基准测试中进行了大量实证研究，证明了所提方法具有高效的有希望的性能。

高效长短时注意力网络用于无监督视频目标分割

Efficient Long-Short Temporal Attention Network for Unsupervised Video  Object Segmentation

To accurately predict future positions of different agents in traffic
scenarios is crucial for safely deploying intelligent autonomous systems in the
real-world environment. However, it remains a challenge due to the behavior of
a target agent being affected by other agents dynamically and there being more
than one socially possible paths the agent could take. In this paper, we
propose a novel framework, named Dynamic Context Encoder Network (DCENet). In
our framework, first, the spatial context between agents is explored by using
self-attention architectures. Then, the two-stream encoders are trained to
learn temporal context between steps by taking the respective observed
trajectories and the extracted dynamic spatial context as input. The
spatial-temporal context is encoded into a latent space using a Conditional
Variational Auto-Encoder (CVAE) module. Finally, a set of future trajectories
for each agent is predicted conditioned on the learned spatial-temporal context
by sampling from the latent space, repeatedly. DCENet is evaluated on one of
the most popular challenging benchmarks for trajectory forecasting Trajnet and
reports a new state-of-the-art performance. It also demonstrates superior
performance evaluated on the benchmark inD for mixed traffic at intersections.
A series of ablation studies is conducted to validate the effectiveness of each
proposed module. Our code is available at this https URL

本文提出了一种名为 Dynamic Context Encoder Network (DCENet) 的新型框架，用于探索交通场景中代理之间的空间和时间上下文信息，从而提高未来位置的预测准确性，实现更安全的自主驾驶系统。DCENet 在多个基准测试中都表现出了良好的性能，并通过一系列实验验证了提出的模块的有效性。

探索多路径轨迹预测的动态上下文

Exploring Dynamic Context for Multi-path Trajectory Prediction

Person re-identification (reID) by CNNs based networks has achieved favorable
performance in recent years. However, most of existing CNNs based methods do
not take full advantage of spatial-temporal context modeling. In fact, the
global spatial-temporal context can greatly clarify local distractions to
enhance the target feature representation. To comprehensively leverage the
spatial-temporal context information, in this work, we present a novel block,
Interaction-Aggregation-Update (IAU), for high-performance person reID.
Firstly, Spatial-Temporal IAU (STIAU) module is introduced. STIAU jointly
incorporates two types of contextual interactions into a CNN framework for
target feature learning. Here the spatial interactions learn to compute the
contextual dependencies between different body parts of a single frame. While
the temporal interactions are used to capture the contextual dependencies
between the same body parts across all frames. Furthermore, a Channel IAU
(CIAU) module is designed to model the semantic contextual interactions between
channel features to enhance the feature representation, especially for
small-scale visual cues and body parts. Therefore, the IAU block enables the
feature to incorporate the globally spatial, temporal, and channel context. It
is lightweight, end-to-end trainable, and can be easily plugged into existing
CNNs to form IAUnet. The experiments show that IAUnet performs favorably
against state-of-the-art on both image and video reID tasks and achieves
compelling results on a general object categorization task. The source code is
available at this https URL

本文提出了一种新的块，IAU，它综合利用时空信息以提高 person reID 任务的性能，并通过轻量级、端到端的设计，可以轻松插入到现有的 CNN 中。实验表明，IAU 在图像和视频重新识别任务上表现优异，并在一般对象分类任务上取得了令人信服的结果。