Analysis of the 3D Texture is indispensable for various tasks, such as
retrieval, segmentation, classification, and inspection of sculptures, knitted
fabrics, and biological tissues. A 3D texture is a locally repeated surface
variation independent of the surface's overall shape and can be determined
using the local neighborhood and its characteristics. Existing techniques
typically employ computer vision techniques that analyze a 3D mesh globally,
derive features, and then utilize the obtained features for retrieval or
classification. Several traditional and learning-based methods exist in the
literature, however, only a few are on 3D texture, and nothing yet, to the best
of our knowledge, on the unsupervised schemes. This paper presents an original
framework for the unsupervised segmentation of the 3D texture on the mesh
manifold. We approach this problem as binary surface segmentation, partitioning
the mesh surface into textured and non-textured regions without prior
annotation. We devise a mutual transformer-based system comprising a label
generator and a cleaner. The two models take geometric image representations of
the surface mesh facets and label them as texture or non-texture across an
iterative mutual learning scheme. Extensive experiments on three publicly
available datasets with diverse texture patterns demonstrate that the proposed
framework outperforms standard and SOTA unsupervised techniques and competes
reasonably with supervised methods.

该研究提出了一种原始框架，用于在网格流形上对 3D 纹理进行无监督分割，通过相互变换的系统将网格表面划分为纹理区域和非纹理区域，使用几何图像表示对网格面片进行标记，并在三个公开数据集上进行大量实验证明该框架优于标准和 SOTA 无监督技术，并且与有监督方法具有合理的竞争力。

3D-TexSeg: 基于互补式转换学习的 3D 纹理无监督分割

3D-TexSeg: Unsupervised Segmentation of 3D Texture using Mutual  Transformer Learning

Knowledge Distillation (KD) has been one of the most popu-lar methods to
learn a compact model. However, it still suffers from highdemand in time and
computational resources caused by sequential train-ing pipeline. Furthermore,
the soft targets from deeper models do notoften serve as good cues for the
shallower models due to the gap of com-patibility. In this work, we consider
these two problems at the same time.Specifically, we propose that better soft
targets with higher compatibil-ity can be generated by using a label generator
to fuse the feature mapsfrom deeper stages in a top-down manner, and we can
employ the meta-learning technique to optimize this label generator. Utilizing
the softtargets learned from the intermediate feature maps of the model, we
canachieve better self-boosting of the network in comparison with the
state-of-the-art. The experiments are conducted on two standard
classificationbenchmarks, namely CIFAR-100 and ILSVRC2012. We test various
net-work architectures to show the generalizability of our MetaDistiller.
Theexperiments results on two datasets strongly demonstrate the effective-ness
of our method.

本文提出使用标签生成器以元学习技术优化生成更优软目标，然后利用中间特征图学习软目标来实现网络的自我增强，从而在网络模型压缩方面达到更好的效果，并在两个常见的基准数据集上进行了实验，结果表明该方法的有效性。

MetaDistiller: 借助元学习自我提升的自顶向下蒸馏网络

MetaDistiller: Network Self-Boosting via Meta-Learned Top-Down  Distillation

The noisy labeling problem has been one of the major obstacles for distant
supervised relation extraction. Existing approaches usually consider that the
noisy sentences are useless and will harm the model's performance. Therefore,
they mainly alleviate this problem by reducing the influence of noisy
sentences, such as applying bag-level selective attention or removing noisy
sentences from sentence-bags. However, the underlying cause of the noisy
labeling problem is not the lack of useful information, but the missing
relation labels. Intuitively, if we can allocate credible labels for noisy
sentences, they will be transformed into useful training data and benefit the
model's performance. Thus, in this paper, we propose a novel method for distant
supervised relation extraction, which employs unsupervised deep clustering to
generate reliable labels for noisy sentences. Specifically, our model contains
three modules: a sentence encoder, a noise detector and a label generator. The
sentence encoder is used to obtain feature representations. The noise detector
detects noisy sentences from sentence-bags, and the label generator produces
high-confidence relation labels for noisy sentences. Extensive experimental
results demonstrate that our model outperforms the state-of-the-art baselines
on a popular benchmark dataset, and can indeed alleviate the noisy labeling
problem.

本文提出了一种利用无监督深度聚类技术解决噪声标注问题的远程监督关系抽取模型，包括句子编码器、噪声检测器和标签生成器，实验证明该模型在常用基准数据集上优于现有的同类模型，并能够应对噪声标注问题。