3D Gaussian Splatting (3DGS) creates a radiance field consisting of 3D
Gaussians to represent a scene. With sparse training views, 3DGS easily suffers
from overfitting, negatively impacting the reconstruction quality. This paper
introduces a new co-regularization perspective for improving sparse-view 3DGS.
When training two 3D Gaussian radiance fields with the same sparse views of a
scene, we observe that the two radiance fields exhibit \textit{point
disagreement} and \textit{rendering disagreement} that can unsupervisedly
predict reconstruction quality, stemming from the sampling implementation in
densification. We further quantify the point disagreement and rendering
disagreement by evaluating the registration between Gaussians' point
representations and calculating differences in their rendered pixels. The
empirical study demonstrates the negative correlation between the two
disagreements and accurate reconstruction, which allows us to identify
inaccurate reconstruction without accessing ground-truth information. Based on
the study, we propose CoR-GS, which identifies and suppresses inaccurate
reconstruction based on the two disagreements: (\romannumeral1) Co-pruning
considers Gaussians that exhibit high point disagreement in inaccurate
positions and prunes them. (\romannumeral2) Pseudo-view co-regularization
considers pixels that exhibit high rendering disagreement are inaccurately
rendered and suppress the disagreement. Results on LLFF, Mip-NeRF360, DTU, and
Blender demonstrate that CoR-GS effectively regularizes the scene geometry,
reconstructs the compact representations, and achieves state-of-the-art novel
view synthesis quality under sparse training views.

该论文介绍了一种新的协同正则化方法来改善稀疏视图下 3D 高斯光场的重建质量，通过评估高斯点表示之间的注册以及渲染像素之间的差异来量化点和渲染不一致性，进一步研究表明这两种不一致性与精确的重建之间存在负相关，基于这些研究结果，提出了一种名为 CoR-GS 的方法来鉴别和抑制不准确的重建，并在多个数据集上验证了该方法的有效性。

CoR-GS：稀疏观测三维高斯点云绘制的协同正则化

CoR-GS: Sparse-View 3D Gaussian Splatting via Co-Regularization

Recent information extraction approaches have relied on training deep neural
models. However, such models can easily overfit noisy labels and suffer from
performance degradation. While it is very costly to filter noisy labels in
large learning resources, recent studies show that such labels take more
training steps to be memorized and are more frequently forgotten than clean
labels, therefore are identifiable in training. Motivated by such properties,
we propose a simple co-regularization framework for entity-centric information
extraction, which consists of several neural models with identical structures
but different parameter initialization. These models are jointly optimized with
the task-specific losses and are regularized to generate similar predictions
based on an agreement loss, which prevents overfitting on noisy labels.
Extensive experiments on two widely used but noisy benchmarks for information
extraction, TACRED and CoNLL03, demonstrate the effectiveness of our framework.
We release our code to the community for future research.

本文研究信息提取中遇到的嘈杂标签问题，提出了一个共同正则化框架来解决这个问题，该框架基于识别嘈杂标签需要更多训练步骤且更容易被忘记的性质，在 TACRED 和 CoNLL03 这两个嘈杂基准数据集上进行了广泛的实验，并发布了代码供未来研究使用。

面向实体中心信息抽取的噪声标签学习

Learning from Noisy Labels for Entity-Centric Information Extraction

Facial action units (AUs) recognition is essential for emotion analysis and
has been widely applied in mental state analysis. Existing work on AU
recognition usually requires big face dataset with AU labels; however, manual
AU annotation requires expertise and can be time-consuming. In this work, we
propose a semi-supervised approach for AU recognition utilizing a large number
of web face images without AU labels and a relatively small face dataset with
AU annotations inspired by the co-training methods. Unlike traditional
co-training methods that require provided multi-view features and model
re-training, we propose a novel co-training method, namely multi-label
co-regularization, for semi-supervised facial AU recognition. Two deep neural
networks are utilized to generate multi-view features for both labeled and
unlabeled face images, and a multi-view loss is designed to enforce the two
feature generators to get conditional independent representations. In order to
constrain the prediction consistency of the two views, we further propose a
multi-label co-regularization loss by minimizing the distance of the predicted
AU probability distributions of two views. In addition, prior knowledge of the
relationship between individual AUs is embedded through a graph convolutional
network (GCN) for exploiting useful information from the big unlabeled dataset.
Experiments on several benchmarks show that the proposed approach can
effectively leverage large datasets of face images without AU labels to improve
the AU recognition accuracy and outperform the state-of-the-art semi-supervised
AU recognition methods.

本文提出了一种基于深度神经网络、半监督学习和图卷积网络的面部动作单元识别方法，可以通过较少的有注释 AU 信息的面部数据和大量的没有注释 AU 信息的网络图像数据共同训练提高 AU 识别的准确性。