Referring video object segmentation (RVOS) aims to segment the target
instance referred by a given text expression in a video clip. The text
expression normally contains sophisticated descriptions of the instance's
appearance, actions, and relations with others. It is therefore rather
difficult for an RVOS model to capture all these attributes correspondingly in
the video; in fact, the model often favours more on the action- and
relation-related visual attribute of the instance. This can end up with
incomplete or even incorrect mask prediction of the target instance. In this
paper, we tackle this problem by taking a subject-centric short text expression
from the original long text expression. The short one retains only the
appearance-related information of the target instance so that we can use it to
focus the model's attention on the instance's appearance. We let the model make
joint predictions using both long and short text expressions and introduce a
long-short predictions intersection loss to align the joint predictions.
Besides the improvement on the linguistic part, we also introduce a
forward-backward visual consistency loss, which utilizes optical flows to warp
visual features between the annotated frames and their temporal neighbors for
consistency. We build our method on top of two state of the art
transformer-based pipelines for end-to-end training. Extensive experiments on
A2D-Sentences and JHMDB-Sentences datasets show impressive improvements of our
method.

本文提出了一种基于短文本表达的参考视频对象分割方法，结合长短文本表达进行联合预测，并引入了一种前向 - 后向视觉一致性损失，实现了在 A2D-Sentences 和 JHMDB-Sentences 数据集上表现出的显著性改进。

长短文本联合预测网络：应用于指代视频目标分割

LoSh: Long-Short Text Joint Prediction Network for Referring Video  Object Segmentation

Predicting future behaviors of road agents is a key task in autonomous
driving. While existing models have demonstrated great success in predicting
marginal agent future behaviors, it remains a challenge to efficiently predict
consistent joint behaviors of multiple agents. Recently, the occupancy flow
fields representation was proposed to represent joint future states of road
agents through a combination of occupancy grid and flow, which supports
efficient and consistent joint predictions. In this work, we propose a novel
occupancy flow fields predictor to produce accurate occupancy and flow
predictions, by combining the power of an image encoder that learns features
from a rasterized traffic image and a vector encoder that captures information
of continuous agent trajectories and map states. The two encoded features are
fused by multiple attention modules before generating final predictions. Our
simple but effective model ranks 3rd place on the Waymo Open Dataset Occupancy
and Flow Prediction Challenge, and achieves the best performance in the
occluded occupancy and flow prediction task.

本文提出了一种新的占据流场预测算法，该算法将图像编码器和向量编码器的特征进行融合以生成更准确的占据和流量预测，该算法在 Waymo Open Dataset Occupancy and Flow Prediction Challenge 中排名第三，在遮挡情况下具有最佳的占据和流量预测表现。

VectorFlow：结合图像和向量进行交通占用和流量预测

VectorFlow: Combining Images and Vectors for Traffic Occupancy and Flow Prediction

A fundamental challenge for any intelligent system is prediction: given some
inputs, can you predict corresponding outcomes? Most work on supervised
learning has focused on producing accurate marginal predictions for each input.
However, we show that for a broad class of decision problems, accurate joint
predictions are required to deliver good performance. In particular, we
establish several results pertaining to combinatorial decision problems,
sequential predictions, and multi-armed bandits to elucidate the essential role
of joint predictive distributions. Our treatment of multi-armed bandits
introduces an approximate Thompson sampling algorithm and analytic techniques
that lead to a new kind of regret bound.

智能系统需要进行预测，在给定一些输入的情况下，可以预测相应的结果。大部分监督学习的工作都侧重于为每个输入产生准确的边际预测，但我们表明，在广泛的决策问题类中，需要准确的联合预测才能提供良好的性能。尤其是对组合决策问题，顺序预测和多臂老虎机等问题，我们建立了几个相关结果，说明联合预测分布的重要作用。我们的多臂老虎机处理引入了一种近似 Thompson 抽样算法和分析技术，导致了一种新的遗憾边界。