Deep implicit functions (DIFs) have emerged as a potent and articulate means
of representing 3D shapes. However, methods modeling object categories or
non-rigid entities have mainly focused on single-object scenarios. In this
work, we propose MODIF, a multi-object deep implicit function that jointly
learns the deformation fields and instance-specific latent codes for multiple
objects at once. Our emphasis is on non-rigid, non-interpenetrating entities
such as organs. To effectively capture the interrelation between these entities
and ensure precise, collision-free representations, our approach facilitates
signaling between category-specific fields to adequately rectify shapes. We
also introduce novel inter-object supervision: an attraction-repulsion loss is
formulated to refine contact regions between objects. Our approach is
demonstrated on various medical benchmarks, involving modeling different groups
of intricate anatomical entities. Experimental results illustrate that our
model can proficiently learn the shape representation of each organ and their
relations to others, to the point that shapes missing from unseen instances can
be consistently recovered by our method. Finally, MODIF can also propagate
semantic information throughout the population via accurate point
correspondences

通过多物体深度隐式函数模型（MODIF），我们提出了一种学习多个非刚性实例的变形场和实例特定潜在代码的方法，以实现精确、无碰撞的形状表示，并在医学基准测试中展示了其优越性能。

非刚性物体的跨类别信号隐式建模

Implicit Modeling of Non-rigid Objects with Cross-Category Signals

Multi-object multi-part scene parsing is a challenging task which requires
detecting multiple object classes in a scene and segmenting the semantic parts
within each object. In this paper, we propose FLOAT, a factorized label space
framework for scalable multi-object multi-part parsing. Our framework involves
independent dense prediction of object category and part attributes which
increases scalability and reduces task complexity compared to the monolithic
label space counterpart. In addition, we propose an inference-time 'zoom'
refinement technique which significantly improves segmentation quality,
especially for smaller objects/parts. Compared to state of the art, FLOAT
obtains an absolute improvement of 2.0% for mean IOU (mIOU) and 4.8% for
segmentation quality IOU (sqIOU) on the Pascal-Part-58 dataset. For the larger
Pascal-Part-108 dataset, the improvements are 2.1% for mIOU and 3.9% for sqIOU.
We incorporate previously excluded part attributes and other minor parts of the
Pascal-Part dataset to create the most comprehensive and challenging version
which we dub Pascal-Part-201. FLOAT obtains improvements of 8.6% for mIOU and
7.5% for sqIOU on the new dataset, demonstrating its parsing effectiveness
across a challenging diversity of objects and parts. The code and datasets are
available at floatseg.github.io.

本文提出 FLOAT 框架，它是一个因式化的标签空间框架，可以有效地实现多对象和多部分图像场景的分割和语义分析。FLOAT 的独立密集预测对象和部分属性，减少了任务复杂度并提高了可扩展性，而推理时的 “缩放” 细化技术可以显著提高细分质量。与现有技术相比，FLOAT 在 Pascal-Part-58、Pascal-Part-108 数据集上，分别实现了 2.0%、2.1% 的 mIOU 和 4.8%、3.9% 的 sqIOU 的绝对提升。我们使用 Pascal-Part 数据集来创建最全面和具有挑战性的数据集 Pascal-Part-201，并实现 8.6% 的 mIOU 和 7.5% 的 sqIOU 的改进，证明了 FLOAT 在对象和零件的多样性中的有效分割。

FLOAT: 改善多目标多部件场景分析的因式学习对象属性

FLOAT: Factorized Learning of Object Attributes for Improved  Multi-object Multi-part Scene Parsing

Unsupervised multi-object scene decomposition is a fast-emerging problem in
representation learning. Despite significant progress in static scenes, such
models are unable to leverage important dynamic cues present in video. We
propose a novel spatio-temporal iterative inference framework that is powerful
enough to jointly model complex multi-object representations and explicit
temporal dependencies between latent variables across frames. This is achieved
by leveraging 2D-LSTM, temporally conditioned inference and generation within
the iterative amortized inference for posterior refinement. Our method improves
the overall quality of decompositions, encodes information about the objects'
dynamics, and can be used to predict trajectories of each object separately.
Additionally, we show that our model has a high accuracy even without color
information. We demonstrate the decomposition, segmentation, and prediction
capabilities of our model and show that it outperforms the state-of-the-art on
several benchmark datasets, one of which was curated for this work and will be
made publicly available.

该研究提出了一种新的时空迭代推理框架，用于联合建模复杂的多物体表示和帧间的显式时间依赖关系，在后验精化中利用 2D-LSTM 和时间条件推理和生成，以提高场景分解、分割和预测的能力，即使没有颜色信息也有高准确度。