Morphable models are fundamental to numerous human-centered processes as they
offer a simple yet expressive shape space. Creating such morphable models,
however, is both tedious and expensive. The main challenge is establishing
dense correspondences across raw scans that capture sufficient shape variation.
This is often addressed using a mix of significant manual intervention and
non-rigid registration. We observe that creating a shape space and solving for
dense correspondence are tightly coupled -- while dense correspondence is
needed to build shape spaces, an expressive shape space provides a reduced
dimensional space to regularize the search. We introduce BLiSS, a method to
solve both progressively. Starting from a small set of manually registered
scans to bootstrap the process, we enrich the shape space and then use that to
get new unregistered scans into correspondence automatically. The critical
component of BLiSS is a non-linear deformation model that captures details
missed by the low-dimensional shape space, thus allowing progressive enrichment
of the space.

BLiSS 是一种解决建立形状空间和密集对应的方法，通过使用非线性变形模型捕捉细节，实现对形状空间的逐步丰富。

BLiSS: 自举线性形状空间

BLiSS: Bootstrapped Linear Shape Space

Data augmentations (DA) are the cores to achieving robust
sequence-to-sequence learning on various natural language processing (NLP)
tasks. However, most of the DA approaches force the decoder to make predictions
conditioned on the perturbed input representation, underutilizing supervised
information provided by perturbed input. In this work, we propose a
framework-level robust sequence-to-sequence learning approach, named BLISS, via
self-supervised input representation, which has the great potential to
complement the data-level augmentation approaches. The key idea is to supervise
the sequence-to-sequence framework with both the \textit{supervised}
("input$\rightarrow$output") and \textit{self-supervised} ("perturbed
input$\rightarrow$input") information. We conduct comprehensive experiments to
validate the effectiveness of BLISS on various tasks, including machine
translation, grammatical error correction, and text summarization. The results
show that BLISS outperforms significantly the vanilla Transformer and
consistently works well across tasks than the other five contrastive baselines.
Extensive analyses reveal that BLISS learns robust representations and rich
linguistic knowledge, confirming our claim. Source code will be released upon
publication.

本文提出了一种名为 BLISS 的架构级强韧序列到序列学习方法，通过自监督输入表示具有潜在的补充数据级扩充方法的潜力，在各种任务中得到验证，包括机器翻译，语法纠错和文本摘要。