We introduce SPAFormer, an innovative model designed to overcome the
combinatorial explosion challenge in the 3D Part Assembly (3D-PA) task. This
task requires accurate prediction of each part's pose and shape in sequential
steps, and as the number of parts increases, the possible assembly combinations
increase exponentially, leading to a combinatorial explosion that severely
hinders the efficacy of 3D-PA. SPAFormer addresses this problem by leveraging
weak constraints from assembly sequences, effectively reducing the solution
space's complexity. Since assembly part sequences convey construction rules
similar to sentences being structured through words, our model explores both
parallel and autoregressive generation. It further enhances assembly through
knowledge enhancement strategies that utilize the attributes of parts and their
sequence information, enabling it to capture the inherent assembly pattern and
relationships among sequentially ordered parts. We also construct a more
challenging benchmark named PartNet-Assembly covering 21 varied categories to
more comprehensively validate the effectiveness of SPAFormer. Extensive
experiments demonstrate the superior generalization capabilities of SPAFormer,
particularly with multi-tasking and in scenarios requiring long-horizon
assembly. Codes and model weights will be released at
https://github.com/xuboshen/SPAFormer.

SPAFormer 是一种创新型模型，旨在解决 3D 零件组装任务中的组合爆炸挑战，通过利用装配序列的弱约束来降低解空间的复杂性，提供更好的通用性能和长期视野组装的策略。

SPAFormer：使用 Transformer 进行顺序 3D 零件组装

SPAFormer: Sequential 3D Part Assembly with Transformers

Decision trees offer the benefit of easy interpretation because they allow
the classification of input data based on if--then rules. However, as decision
trees are constructed by an algorithm that achieves clear classification with
minimum necessary rules, the trees possess the drawback of extracting only
minimum rules, even when various latent rules exist in data. Approaches that
construct multiple trees using randomly selected feature subsets do exist.
However, the number of trees that can be constructed remains at the same scale
because the number of feature subsets is a combinatorial explosion.
Additionally, when multiple trees are constructed, numerous rules are
generated, of which several are untrustworthy and/or highly similar. Therefore,
we propose "MAABO-MT" and "GS-MRM" algorithms that strategically construct
trees with high estimation performance among all possible trees with small
computational complexity and extract only reliable and non-similar rules,
respectively. Experiments are conducted using several open datasets to analyze
the effectiveness of the proposed method. The results confirm that MAABO-MT can
discover reliable rules at a lower computational cost than other methods that
rely on randomness. Furthermore, the proposed method is confirmed to provide
deeper insights than single decision trees commonly used in previous studies.
Therefore, MAABO-MT and GS-MRM can efficiently extract rules from
combinatorially exploded decision trees.

决策树具有易于解释的优点，因为它们允许根据 if-then 规则对输入数据进行分类。然而，决策树是由算法构建的，该算法在达到清晰分类的同时使用最少的必要规则，因此，即使数据中存在各种潜在规则，树也只提取最少规则。为了解决这个问题，本文提出了 “MAABO-MT” 和 “GS-MRM” 算法，它们分别在具有小计算复杂性的所有可能树中构建具有高估计性能的树，以及仅提取可靠且不相似的规则。通过使用几个开源数据集进行实验分析，结果证实了 MAABO-MT 以较低的计算代价发现可靠规则，比其他依赖随机性的方法更为有效。此外，所提出的方法被证实在深入挖掘相比之前常用的单一决策树的研究中提供更深入的见解。因此，MAABO-MT 和 GS-MRM 能够高效地从组合爆炸的决策树中提取规则。

基于修改的 Aitchison-Aitken 函数的贝叶斯优化和组合扩展决策树的多规则挖掘算法

Multi-rules mining algorithm for combinatorially exploded decision trees  with modified Aitchison-Aitken function-based Bayesian optimization

This is an opinion paper about the strengths and weaknesses of Deep Nets for
vision. They are at the heart of the enormous recent progress in artificial
intelligence and are of growing importance in cognitive science and
neuroscience. They have had many successes but also have several limitations
and there is limited understanding of their inner workings. At present Deep
Nets perform very well on specific visual tasks with benchmark datasets but
they are much less general purpose, flexible, and adaptive than the human
visual system. We argue that Deep Nets in their current form are unlikely to be
able to overcome the fundamental problem of computer vision, namely how to deal
with the combinatorial explosion, caused by the enormous complexity of natural
images, and obtain the rich understanding of visual scenes that the human
visual achieves. We argue that this combinatorial explosion takes us into a
regime where "big data is not enough" and where we need to rethink our methods
for benchmarking performance and evaluating vision algorithms. We stress that,
as vision algorithms are increasingly used in real world applications, that
performance evaluation is not merely an academic exercise but has important
consequences in the real world. It is impractical to review the entire Deep Net
literature so we restrict ourselves to a limited range of topics and references
which are intended as entry points into the literature. The views expressed in
this paper are our own and do not necessarily represent those of anybody else
in the computer vision community.

本文讨论了当前深度神经网络在计算机视觉领域的优势与劣势，并探讨了如何解决计算机视觉中所面临的组合爆炸问题。我们认为，当前的深度神经网络很难克服这个困难，因此需要重新思考我们评估算法性能的方法。