This paper introduces GS-Pose, an end-to-end framework for locating and
estimating the 6D pose of objects. GS-Pose begins with a set of posed RGB
images of a previously unseen object and builds three distinct representations
stored in a database. At inference, GS-Pose operates sequentially by locating
the object in the input image, estimating its initial 6D pose using a retrieval
approach, and refining the pose with a render-and-compare method. The key
insight is the application of the appropriate object representation at each
stage of the process. In particular, for the refinement step, we utilize 3D
Gaussian splatting, a novel differentiable rendering technique that offers high
rendering speed and relatively low optimization time. Off-the-shelf toolchains
and commodity hardware, such as mobile phones, can be used to capture new
objects to be added to the database. Extensive evaluations on the LINEMOD and
OnePose-LowTexture datasets demonstrate excellent performance, establishing the
new state-of-the-art. Project page: this https URL

GS-Pose 是一个端到端的定位和估计物体 6D 姿态的框架，通过构建三种不同的表示形式，利用逐步定位、检索方法和渲染比较方法，以适应各个阶段的合适物体表示，并利用可用的工具链和常见硬件进行新对象的数据捕捉和数据库添加。在 LINEMOD 和 OnePose-LowTexture 数据集上进行广泛评估，取得了卓越的性能，建立了新的最先进技术。

GS-Pose：通用分割 6D 物体位姿估计的级联框架

GS-Pose: Cascaded Framework for Generalizable Segmentation-based 6D  Object Pose Estimation

Text classification in education, usually called auto-tagging, is the
automated process of assigning relevant tags to educational content, such as
questions and textbooks. However, auto-tagging suffers from a data scarcity
problem, which stems from two major challenges: 1) it possesses a large tag
space and 2) it is multi-label. Though a retrieval approach is reportedly good
at low-resource scenarios, there have been fewer efforts to directly address
the data scarcity problem. To mitigate these issues, here we propose a novel
retrieval approach CEAA that provides effective learning in educational text
classification. Our main contributions are as follows: 1) we leverage transfer
learning from question-answering datasets, and 2) we propose a simple but
effective data augmentation method introducing cross-encoder style texts to a
bi-encoder architecture for more efficient inference. An extensive set of
experiments shows that our proposed method is effective in multi-label
scenarios and low-resource tags compared to state-of-the-art models.

本文提出了一种检索方法 CEAA，可以有效解决教育文本分类中的多标签、低资源等数据匮乏问题，其主要贡献是将迁移学习和跨编码器样式文本引入到双编码器结构中，以提高推理效率。实验表明，该方法比现有模型更有效。

跨编码作为增强方式：朝着有效的教育文本分类

Cross Encoding as Augmentation: Towards Effective Educational Text  Classification

Visual Place Recognition is a task that aims to predict the coordinates of an
image (called query) based solely on visual clues. Most commonly, a retrieval
approach is adopted, where the query is matched to the most similar images from
a large database of geotagged photos, using learned global descriptors. Despite
recent advances, recognizing the same place when the query comes from a
significantly different distribution is still a major hurdle for state of the
art retrieval methods. Examples are heavy illumination changes (e.g. night-time
images) or substantial occlusions (e.g. transient objects). In this work we
explore whether re-ranking methods based on spatial verification can tackle
these challenges, following the intuition that local descriptors are inherently
more robust than global features to domain shifts. To this end, we provide a
new, comprehensive benchmark on current state of the art models. We also
introduce two new demanding datasets with night and occluded queries, to be
matched against a city-wide database. Code and datasets are available at
this https URL

本文研究使用基于空间验证的重新排序方法尝试解决视觉地方识别中光照和遮挡等困难问题，并给出了新的综合基准和两个具有挑战性的数据集。