Semi-supervised video object segmentation (semi-VOS) is widely used in many
applications. This task is tracking class-agnostic objects from a given target
mask. For doing this, various approaches have been developed based on
online-learning, memory networks, and optical flow. These methods show high
accuracy but are hard to be utilized in real-world applications due to slow
inference time and tremendous complexity. To resolve this problem, template
matching methods are devised for fast processing speed but sacrificing lots of
performance in previous models. We introduce a novel semi-VOS model based on a
template matching method and a temporal consistency loss to reduce the
performance gap from heavy models while expediting inference time a lot. Our
template matching method consists of short-term and long-term matching. The
short-term matching enhances target object localization, while long-term
matching improves fine details and handles object shape-changing through the
newly proposed adaptive template attention module. However, the long-term
matching causes error-propagation due to the inflow of the past estimated
results when updating the template. To mitigate this problem, we also propose a
temporal consistency loss for better temporal coherence between neighboring
frames by adopting the concept of a transition matrix. Our model obtains 79.5%
J&F score at the speed of 73.8 FPS on the DAVIS16 benchmark. The code is
available in this https URL

本文提出了一种基于模板匹配和时域一致性损失的半监督视频对象分割模型，以减少复杂性并加快推理时间，既提高了目标对象的定位，又能够处理细节，并通过新的自适应模板注意模块处理对象形状变化。该模型在 DAVIS16 基准上获得了 79.5% 的 J＆F 分数，速度为 73.8 FPS。

TTVOS: 自适应模板注意力模块和时序一致性损失的轻量级视频目标分割

TTVOS: Lightweight Video Object Segmentation with Adaptive Template  Attention Module and Temporal Consistency Loss

In this paper, we investigate the problem of unpaired video-to-video
translation. Given a video in the source domain, we aim to learn the
conditional distribution of the corresponding video in the target domain,
without seeing any pairs of corresponding videos. While significant progress
has been made in the unpaired translation of images, directly applying these
methods to an input video leads to low visual quality due to the additional
time dimension. In particular, previous methods suffer from semantic
inconsistency (i.e., semantic label flipping) and temporal flickering
artifacts. To alleviate these issues, we propose a new framework that is
composed of carefully-designed generators and discriminators, coupled with two
core objective functions: 1) content preserving loss and 2) temporal
consistency loss. Extensive qualitative and quantitative evaluations
demonstrate the superior performance of the proposed method against previous
approaches. We further apply our framework to a domain adaptation task and
achieve favorable results.

本文研究了无配对视频到视频翻译的问题，提出了一种新的框架，包括经过精心设计的生成器和鉴别器，以及两个核心目标函数：内容保持损失和时间一致性损失。经过广泛的定性和定量评估，证明了该方法相对于先前的方法具有更优越的性能。