In this paper, we study the problem of end-to-end multi-person pose
estimation. State-of-the-art solutions adopt the DETR-like framework, and
mainly develop the complex decoder, e.g., regarding pose estimation as keypoint
box detection and combining with human detection in ED-Pose, hierarchically
predicting with pose decoder and joint (keypoint) decoder in PETR. We present a
simple yet effective transformer approach, named Group Pose. We simply regard
$K$-keypoint pose estimation as predicting a set of $N\times K$ keypoint
positions, each from a keypoint query, as well as representing each pose with
an instance query for scoring $N$ pose predictions. Motivated by the intuition
that the interaction, among across-instance queries of different types, is not
directly helpful, we make a simple modification to decoder self-attention. We
replace single self-attention over all the $N\times(K+1)$ queries with two
subsequent group self-attentions: (i) $N$ within-instance self-attention, with
each over $K$ keypoint queries and one instance query, and (ii) $(K+1)$
same-type across-instance self-attention, each over $N$ queries of the same
type. The resulting decoder removes the interaction among across-instance
type-different queries, easing the optimization and thus improving the
performance. Experimental results on MS COCO and CrowdPose show that our
approach without human box supervision is superior to previous methods with
complex decoders, and even is slightly better than ED-Pose that uses human box
supervision. $\href{this https URL}{\rm
Paddle}$ and $\href{this https URL}{\rm PyTorch}$ code
are available.

通过简单且有效的 Transformer 方法，命名为 Group Pose，我们研究了无人工盒监督的端到端多人姿态估计问题，取得了优于复杂解码器的性能。

群组姿势：一种简单的端到端多人姿势估计的基线

Group Pose: A Simple Baseline for End-to-End Multi-person Pose  Estimation

Role-oriented dialogue summarization is to generate summaries for different
roles in the dialogue, e.g., merchants and consumers. Existing methods handle
this task by summarizing each role's content separately and thus are prone to
ignore the information from other roles. However, we believe that other roles'
content could benefit the quality of summaries, such as the omitted information
mentioned by other roles. Therefore, we propose a novel role interaction
enhanced method for role-oriented dialogue summarization. It adopts cross
attention and decoder self-attention interactions to interactively acquire
other roles' critical information. The cross attention interaction aims to
select other roles' critical dialogue utterances, while the decoder
self-attention interaction aims to obtain key information from other roles'
summaries. Experimental results have shown that our proposed method
significantly outperforms strong baselines on two public role-oriented dialogue
summarization datasets. Extensive analyses have demonstrated that other roles'
content could help generate summaries with more complete semantics and correct
topic structures.

该论文介绍了一种基于角色的对话总结的新方法，采用交叉关注和解码器自我关注交互来获取其他角色的关键信息，证明了其他角色信息可以帮助生成更完整语义和正确主题结构的总结。

其他角色很重要！通过角色交互增强面向角色的对话摘要

Other Roles Matter! Enhancing Role-Oriented Dialogue Summarization via Role Interactions

Non-autoregressive translation (NAT) models suffer from inferior translation
quality due to removal of dependency on previous target tokens from inputs to
the decoder. In this paper, we propose a novel and general approach to enhance
the target dependency within the NAT decoder from two perspectives: decoder
input and decoder self-attention. First, we transform the initial decoder input
from the source language space to the target language space through a novel
attentive transformation process. The transformation reassembles the decoder
input based on target token embeddings and conditions the final output on the
target-side information. Second, before NAT training, we introduce an effective
forward-backward pre-training phase, implemented with different triangle
attention masks. This pre-training phase enables the model to gradually learn
bidirectional dependencies for the final NAT decoding process. Experimental
results demonstrate that the proposed approaches consistently improve highly
competitive NAT models on four WMT translation directions by up to 1.88 BLEU
score, while overall maintaining inference latency comparable to other fully
NAT models.

本文提出了一种能够从两个角度增强神经机器翻译（Non-autoregressive translation）解码器内部的目标依存性的新方法，并在四个 WMT 翻译任务上进行试验。结果表明，该方法相较于其他神经机器翻译方法，能够将 BLEU 得分提高 1.88 分而且性能并不受影响。