We present a method for teaching machines to understand and model the
underlying spatial common sense of diverse human-object interactions in 3D in a
self-supervised way. This is a challenging task, as there exist specific
manifolds of the interactions that can be considered human-like and natural,
but the human pose and the geometry of objects can vary even for similar
interactions. Such diversity makes the annotating task of 3D interactions
difficult and hard to scale, which limits the potential to reason about that in
a supervised way. One way of learning the 3D spatial relationship between
humans and objects during interaction is by showing multiple 2D images captured
from different viewpoints when humans interact with the same type of objects.
The core idea of our method is to leverage a generative model that produces
high-quality 2D images from an arbitrary text prompt input as an "unbounded"
data generator with effective controllability and view diversity. Despite its
imperfection of the image quality over real images, we demonstrate that the
synthesized images are sufficient to learn the 3D human-object spatial
relations. We present multiple strategies to leverage the synthesized images,
including (1) the first method to leverage a generative image model for 3D
human-object spatial relation learning; (2) a framework to reason about the 3D
spatial relations from inconsistent 2D cues in a self-supervised manner via 3D
occupancy reasoning with pose canonicalization; (3) semantic clustering to
disambiguate different types of interactions with the same object types; and
(4) a novel metric to assess the quality of 3D spatial learning of interaction.
Project Page: this https URL

我们提出了一种教导机器以自监督方式理解并建模多种 3D 人物 - 物体相互作用的基础空间常识的方法。我们利用生成模型生成高质量的 2D 图像，并展示了这些合成图像足以学习 3D 人物 - 物体之间的空间关系。

CHORUS: 学习从无限合成图像中的规范化三维人体 - 物体空间关系

CHORUS: Learning Canonicalized 3D Human-Object Spatial Relations from  Unbounded Synthesized Images

Objective: To develop a new method that integrates subspace and generative
image models for high-dimensional MR image reconstruction. Methods: We proposed
a formulation that synergizes a low-dimensional subspace model of
high-dimensional images, an adaptive generative image prior serving as spatial
constraints on the sequence of "contrast-weighted" images or spatial
coefficients of the subspace model, and a conventional sparsity regularization.
A special pretraining plus subject-specific network adaptation strategy was
proposed to construct an accurate generative-network-based representation for
images with varying contrasts. An iterative algorithm was introduced to jointly
update the subspace coefficients and the multi-resolution latent space of the
generative image model that leveraged a recently proposed intermediate layer
optimization technique for network inversion. Results: We evaluated the utility
of the proposed method for two high-dimensional imaging applications:
accelerated MR parameter mapping and high-resolution MR spectroscopic imaging.
Improved performance over state-of-the-art subspace-based methods was
demonstrated in both cases. Conclusion: The proposed method provided a new way
to address high-dimensional MR image reconstruction problems by incorporating
an adaptive generative model as a data-driven spatial prior for constraining
subspace reconstruction. Significance: Our work demonstrated the potential of
integrating data-driven and adaptive generative priors with canonical
low-dimensional modeling for high-dimensional imaging problems.

本研究旨在发展一种新的方法，将子空间和生成图像模型融合到一起以用于高维 MR 图像重建，结果表明该方法在快速 MR 参数映射和高分辨率 MR 光谱成像方面取得了比基于子空间的最新方法更好的性能.

高维 MR 重建：子空间与自适应生成模型集成

High-Dimensional MR Reconstruction Integrating Subspace and Adaptive  Generative Models

Knowledge distillation is one of the most popular and effective techniques
for knowledge transfer, model compression and semi-supervised learning. Most
existing distillation approaches require the access to original or augmented
training samples. But this can be problematic in practice due to privacy,
proprietary and availability concerns. Recent work has put forward some methods
to tackle this problem, but they are either highly time-consuming or unable to
scale to large datasets. To this end, we propose a new method to train a
generative image model by leveraging the intrinsic normalization layers'
statistics of the trained teacher network. This enables us to build an ensemble
of generators without training data that can efficiently produce substitute
inputs for subsequent distillation. The proposed method pushes forward the
data-free distillation performance on CIFAR-10 and CIFAR-100 to 95.02% and
77.02% respectively. Furthermore, we are able to scale it to ImageNet dataset,
which to the best of our knowledge, has never been done using generative models
in a data-free setting.

提出了一种新的方法，通过利用训练教师网络内在归一化层的统计信息来训练生成图像模型，从而在没有训练数据的情况下实现知识迁移、模型压缩和半监督学习，该方法在 CIFAR-10 和 CIFAR-100 数据集上表现出色，并能够将其扩展到 ImageNet 数据集。