Conventional 2D pose estimation models are constrained by their design to
specific object categories. This limits their applicability to predefined
objects. To overcome these limitations, category-agnostic pose estimation
(CAPE) emerged as a solution. CAPE aims to facilitate keypoint localization for
diverse object categories using a unified model, which can generalize from
minimal annotated support images. Recent CAPE works have produced object poses
based on arbitrary keypoint definitions annotated on a user-provided support
image. Our work departs from conventional CAPE methods, which require a support
image, by adopting a text-based approach instead of the support image.
Specifically, we use a pose-graph, where nodes represent keypoints that are
described with text. This representation takes advantage of the abstraction of
text descriptions and the structure imposed by the graph.
Our approach effectively breaks symmetry, preserves structure, and improves
occlusion handling. We validate our novel approach using the MP-100 benchmark,
a comprehensive dataset spanning over 100 categories and 18,000 images. Under a
1-shot setting, our solution achieves a notable performance boost of 1.07\%,
establishing a new state-of-the-art for CAPE. Additionally, we enrich the
dataset by providing text description annotations, further enhancing its
utility for future research.

传统的 2D 姿势估计模型受其设计的限制，仅适用于特定的物体类别，为了克服这些限制，通用类别姿势估计（CAPE）作为解决方案出现，CAPE 旨在使用统一模型为多样的物体类别进行关键点定位，可以从最小的注释支持图像中进行泛化，我们的方法采用了一种不同于常规 CAPE 方法的文本化方法，而不是使用支持图像，具体而言，我们使用姿势图，其中节点表示使用文本进行描述的关键点，这种表示利用文本描述的抽象和图所施加的结构，我们的方法有效地打破了对称性，保留了结构并改善了遮挡处理，我们使用 MP-100 基准验证了我们的新方法，该数据集涵盖 100 多个类别和 18,000 张图像，在 1-shot 设置下，我们的解决方案的性能提升了 1.07％，确立了 CAPE 的新的最先进技术水平，此外，我们通过提供文本描述注释来丰富数据集，进一步增强其对未来研究的实用性。

CapeX: 基于文本点解释的类别无关姿态估计

CapeX: Category-Agnostic Pose Estimation from Textual Point Explanation

Pseudo Relevance Feedback (PRF) is known to improve the effectiveness of
bag-of-words retrievers. At the same time, deep language models have been shown
to outperform traditional bag-of-words rerankers. However, it is unclear how to
integrate PRF directly with emergent deep language models. In this article, we
address this gap by investigating methods for integrating PRF signals into
rerankers and dense retrievers based on deep language models. We consider
text-based and vector-based PRF approaches, and investigate different ways of
combining and scoring relevance signals. An extensive empirical evaluation was
conducted across four different datasets and two task settings (retrieval and
ranking). Text-based PRF results show that the use of PRF had a mixed effect on
deep rerankers across different datasets. We found that the best effectiveness
was achieved when (i) directly concatenating each PRF passage with the query,
searching with the new set of queries, and then aggregating the scores; (ii)
using Borda to aggregate scores from PRF runs. Vector-based PRF results show
that the use of PRF enhanced the effectiveness of deep rerankers and dense
retrievers over several evaluation metrics. We found that higher effectiveness
was achieved when (i) the query retains either the majority or the same weight
within the PRF mechanism, and (ii) a shallower PRF signal (i.e., a smaller
number of top-ranked passages) was employed, rather than a deeper signal. Our
vector-based PRF method is computationally efficient; thus this represents a
general PRF method others can use with deep rerankers and dense retrievers.

本文研究了将 Pseudo Relevance Feedback 方法集成到基于深度语言模型的 rerankers 和 dense retrievers 中的方法，并对基于文本和向量的 PRF 方法进行了考虑和评估。在四个数据集和两个任务设置下进行了广泛的实证评估。结果表明，vector-based PRF 方法有效提高了 deep rerankers 和 dense retrievers 的检索和排名效果。

采用深度语言模型和密集检索器的伪相关反馈：成功与困难

Pseudo Relevance Feedback with Deep Language Models and Dense  Retrievers: Successes and Pitfalls

Document structure extraction has been a widely researched area for decades
with recent works performing it as a semantic segmentation task over document
images using fully-convolution networks. Such methods are limited by image
resolution due to which they fail to disambiguate structures in dense regions
which appear commonly in forms. To mitigate this, we propose Form2Seq, a novel
sequence-to-sequence (Seq2Seq) inspired framework for structure extraction
using text, with a specific focus on forms, which leverages relative spatial
arrangement of structures. We discuss two tasks; 1) Classification of low-level
constituent elements (TextBlock and empty fillable Widget) into ten types such
as field captions, list items, and others; 2) Grouping lower-level elements
into higher-order constructs, such as Text Fields, ChoiceFields and
ChoiceGroups, used as information collection mechanism in forms. To achieve
this, we arrange the constituent elements linearly in natural reading order,
feed their spatial and textual representations to Seq2Seq framework, which
sequentially outputs prediction of each element depending on the final task. We
modify Seq2Seq for grouping task and discuss improvements obtained through
cascaded end-to-end training of two tasks versus training in isolation.
Experimental results show the effectiveness of our text-based approach
achieving an accuracy of 90% on classification task and an F1 of 75.82, 86.01,
61.63 on groups discussed above respectively, outperforming segmentation
baselines. Further we show our framework achieves state of the results for
table structure recognition on ICDAR 2013 dataset.

提出一种基于文本的新颖结构提取框架 Form2Seq，将文档结构提取任务转化为两个子任务：低级元素分类和高级构建分组，通过顺序预测每个元素实现结构提取，使用两个任务级联深度学习训练提高了结果准确性，在 ICDAR 2013 数据集上实现了最佳效果。