Towards holistic understanding of 3D scenes, a general 3D segmentation method
is needed that can segment diverse objects without restrictions on object
quantity or categories, while also reflecting the inherent hierarchical
structure. To achieve this, we propose OmniSeg3D, an omniversal segmentation
method aims for segmenting anything in 3D all at once. The key insight is to
lift multi-view inconsistent 2D segmentations into a consistent 3D feature
field through a hierarchical contrastive learning framework, which is
accomplished by two steps. Firstly, we design a novel hierarchical
representation based on category-agnostic 2D segmentations to model the
multi-level relationship among pixels. Secondly, image features rendered from
the 3D feature field are clustered at different levels, which can be further
drawn closer or pushed apart according to the hierarchical relationship between
different levels. In tackling the challenges posed by inconsistent 2D
segmentations, this framework yields a global consistent 3D feature field,
which further enables hierarchical segmentation, multi-object selection, and
global discretization. Extensive experiments demonstrate the effectiveness of
our method on high-quality 3D segmentation and accurate hierarchical structure
understanding. A graphical user interface further facilitates flexible
interaction for omniversal 3D segmentation.

提出了一种全方位理解 3D 场景的通用 3D 分割方法 OmniSeg3D，通过层次对比学习框架将多视图不一致的 2D 分割提升为一致的 3D 特征场，实现了分层分割、多对象选择和全局离散化，并通过大量实验验证了该方法在高质量 3D 分割和准确定义了层次结构方面的有效性，进一步利用图形用户界面实现了灵活的全方位 3D 分割的交互操作。

OmniSeg3D: 基于层次对比学习的全方位 3D 分割

OmniSeg3D: Omniversal 3D Segmentation via Hierarchical Contrastive  Learning

In this paper, we propose a hierarchical contrastive learning framework,
HiCL, which considers local segment-level and global sequence-level
relationships to improve training efficiency and effectiveness. Traditional
methods typically encode a sequence in its entirety for contrast with others,
often neglecting local representation learning, leading to challenges in
generalizing to shorter texts. Conversely, HiCL improves its effectiveness by
dividing the sequence into several segments and employing both local and global
contrastive learning to model segment-level and sequence-level relationships.
Further, considering the quadratic time complexity of transformers over input
tokens, HiCL boosts training efficiency by first encoding short segments and
then aggregating them to obtain the sequence representation. Extensive
experiments show that HiCL enhances the prior top-performing SNCSE model across
seven extensively evaluated STS tasks, with an average increase of +0.2%
observed on BERT-large and +0.44% on RoBERTa-large.

本文提出了一种层次对比学习框架 HiCL，它考虑了局部分段级和全局序列级关系以提高训练效率和效果。通过将序列分为几个分段，并应用局部和全局对比学习来建模分段级和序列级关系，HiCL 提高了其效果。此外，考虑到输入标记上的 transformers 的二次时间复杂度，在获得序列表示之前，HiCL 通过首先对短分段进行编码，然后将它们聚合起来来提高训练效率。大量实验证明，HiCL 提升了之前表现最好的 SNCSE 模型在七种广泛评估的 STS 任务上的性能，BERT-large 平均提升了 + 0.2%，RoBERTa-large 提升了 + 0.44%。

HiCL: 无监督句子嵌入的分层对比学习

HiCL: Hierarchical Contrastive Learning of Unsupervised Sentence  Embeddings

In task-oriented dialogue scenarios, cross-domain zero-shot slot filling
plays a vital role in leveraging source domain knowledge to learn a model with
high generalization ability in unknown target domain where annotated data is
unavailable. However, the existing state-of-the-art zero-shot slot filling
methods have limited generalization ability in target domain, they only show
effective knowledge transfer on seen slots and perform poorly on unseen slots.
To alleviate this issue, we present a novel Hierarchical Contrastive Learning
Framework (HiCL) for zero-shot slot filling. Specifically, we propose a coarse-
to fine-grained contrastive learning based on Gaussian-distributed embedding to
learn the generalized deep semantic relations between utterance-tokens, by
optimizing inter- and intra-token distribution distance. This encourages HiCL
to generalize to the slot types unseen at training phase. Furthermore, we
present a new iterative label set semantics inference method to unbiasedly and
separately evaluate the performance of unseen slot types which entangled with
their counterparts (i.e., seen slot types) in the previous zero-shot slot
filling evaluation methods. The extensive empirical experiments on four
datasets demonstrate that the proposed method achieves comparable or even
better performance than the current state-of-the-art zero-shot slot filling
approaches.

在任务导向的对话场景中，跨领域零样本槽填充在利用源领域知识学习高泛化能力模型的同时，在未标注数据的未知目标领域中起到至关重要的作用。然而，现有最先进的零样本槽填充方法在目标领域中的泛化能力有限，只对已见槽显示有效的知识转移，在未见槽上表现不佳。为了缓解这一问题，我们提出了一种新颖的分层对比学习框架（HiCL）用于零样本槽填充。具体而言，我们提出了一种基于高斯分布嵌入的粗细粒度对比学习方法，以学习语句标记之间的广义深层语义关系，通过优化之间和之内的标记分布距离。这促使 HiCL 在训练阶段泛化到未见的槽类型上。此外，我们提出了一种新的迭代标签集语义推断方法，可以不偏不倚地分别评估在先前的零样本槽填充评估方法中与它们的对应槽类型相纠缠的未见槽类型的性能。对四个数据集的广泛经验性实验证明，所提出的方法的性能与当前最先进的零样本槽填充方法相当甚至更好。