This work studies the multi-human parsing problem. Existing methods, either
following top-down or bottom-up two-stage paradigms, usually involve expensive
computational costs. We instead present a high-performance Single-stage
Multi-human Parsing (SMP) deep architecture that decouples the multi-human
parsing problem into two fine-grained sub-problems, i.e., locating the human
body and parts. SMP leverages the point features in the barycenter positions to
obtain their segmentation and then generates a series of offsets from the
barycenter of the human body to the barycenters of parts, thus performing human
body and parts matching without the grouping process. Within the SMP
architecture, we propose a Refined Feature Retain module to extract the global
feature of instances through generated mask attention and a Mask of Interest
Reclassify module as a trainable plug-in module to refine the classification
results with the predicted segmentation. Extensive experiments on the MHPv2.0
dataset demonstrate the best effectiveness and efficiency of the proposed
method, surpassing the state-of-the-art method by 2.1% in AP50p, 1.0% in
APvolp, and 1.2% in PCP50. In particular, the proposed method requires fewer
training epochs and a less complex model architecture. We will release our
source codes, pretrained models, and online demos to facilitate further
studies.

该研究提出了一种高性能的单阶段多人解析深度架构，在 MHPv2.0 数据集上实现了最佳的有效性和效率，超过了最先进的方法 2.1％的 AP50p，1.0％的 APvolp 和 1.2％的 PCP50。

基于点集和中心偏移的单阶段多人解析

Single-stage Multi-human Parsing via Point Sets and Center-based Offsets

We study the problem of decomposing a complex text-to-sql task into smaller
sub-tasks and how such a decomposition can significantly improve the
performance of Large Language Models (LLMs) in the reasoning process. There is
currently a significant gap between the performance of fine-tuned models and
prompting approaches using LLMs on challenging text-to-sql datasets such as
Spider. We show that SQL queries, despite their declarative structure, can be
broken down into sub-problems and the solutions of those sub-problems can be
fed into LLMs to significantly improve their performance. Our experiments with
three LLMs show that this approach consistently improves their performance by
roughly 10%, pushing the accuracy of LLMs towards state-of-the-art, and even
beating large fine-tuned models on the holdout Spider dataset.

研究了如何将复杂的文本到 SQL 任务分解为较小的子任务，从而显著提高大型语言模型（LLMs）在推理过程中的性能，证明了将 SQL 查询分解为子问题并将这些子问题的解决方案提供给 LLMs 可以显著提高性能。在三个 LLMs 上的实验表明，此方法始终将性能提高约 10％，推动 LLMs 的准确性接近最先进水平，甚至超过用于持有 Spider 数据集的大型精调模型。