Automatic bone segmentation is a fundamental task supporting various clinical practices. Conventional methods in this field rely heavily on dense annotations, which incurs substantial labeling labor and expense. Recent efforts have been made to reduce the labeling workload through semi-supervised and weakly supervised learning. However, methods under these two paradigms usually assume that all objects of interest e.g., bones, are covered by labels. In this work, we explore a less studied problem setting that assumes only partially labeled bone CT data. To tackle the supervision bias brought by incomplete annotations, we design a three-stage learning method that automatically detects unlabeled bones while being robust to their various shape. Extensive experiments are conducted on the curated dataset to test the proposed method and promising performance is observed. To the best of our knowledge, this is the first work on the partially supervised bone segmentation problem.

Being data-driven is one of the most iconic properties of deep learning algorithms. The birth of ImageNet [24] drives a remarkable trend of ‘learning from large-scale data’ in computer vision. Pretraining on ImageNet to obtain rich universal representations has been manifested to benefit various 2D visual tasks, and becomes a standard in 2D vision. However, due to the laborious collection of real-world 3D data, there is yet no generic dataset serving as a counterpart of ImageNet in 3D vision, thus how such a dataset can impact the 3D community is unraveled. To remedy this defect, we introduce MVImgNet, a large-scale dataset of multi-view images, which is highly convenient to gain by shooting videos of real-world objects in human daily life. It contains 6.5 million frames from 219,188 videos crossing objects from 238 classes, with rich annotations of object masks, camera parameters, and point clouds. The multi-view attribute endows our dataset with 3D-aware signals, making it a soft bridge between 2D and 3D vision. We conduct pilot studies for probing the potential of MVImgNet on a variety of 3D and 2D visual tasks, including radiance field reconstruction, multi-view stereo, and view-consistent image understanding, where MVImgNet demonstrates promising performance, remaining lots of possibilities for future explorations. Besides, via dense reconstruction on MVImgNet, a 3D object point cloud dataset is derived, called MVPNet, covering 87,200 samples from 150 categories, with the class label on each point cloud. Experiments show that MVP-Net can benefit the real-world 3D object classification while posing new challenges to point cloud understanding. MVImgNet and MVPNet will be public, hoping to inspire the broader vision community.

Cross-view consistency is the fundamental property of multiview clustering. However, in incomplete multi-view scenarios, existing methods can only pursue consistency through the paired data while ignoring the information in unpaired data. In this paper, we show a new insight from the data pattern and provide a novel perspective to incorporate unpaired data for consistency maximization by mining group correspondence. We first formulate cross-view consistency in a statistical perspective to by-pass the strict demand of instance correspondence, and then propose a technique to construct corresponding groups across views to enhance the objective of consistency maximization. Our proposal can be used as a universal plug-in to augment existing approaches. We test the efficacy and generality of our proposal by adapting it to two base methods as augmentations and comparing the augmented models against the original ones and other baselines. Experiment results demonstrate the effectiveness of our proposal and validate the value of our insight.

Most existing unsupervised domain adaptation methods learn domain-invariant representations with entangled domain in-formation, semantic information, and instance information. Differently, in this paper, we propose a Triple Disentangling Network (TDN), to disentangle these three types of information and then predict the target labels merely using semantic information. Specifically, TDN consists of a reconstruction module and a disentanglement module. In the reconstruction module, TDN utilizes a variational auto-encoder to re-construct the domain, semantic, and instance latent variables behind the data. In the disentanglement module, adversar-ial learning, discriminative clustering, and instance separation are seamlessly integrated to disentangle these three sets of re-constructed latent variables. Significantly, TDN can not only effectively alleviate the negative transfer of outliers through disentangling instance information, but also disentangle se-mantic information more thoroughly by exploring discriminative structure knowledge. Experimental studies on two bench-mark datasets demonstrate the superiority of TDN.

Unsupervised domain adaptation aims to leverage knowledge from a labeled source domain to learn an accurate model in an unlabeled target domain. However, many previous approaches propose to learn domain agnostic feature representations using a global distribution alignment objective, which does not consider the fine-grained cluster structures in the source and target domains. As such, the goal of this paper is to address two challenging problems:1) how to thoroughly explore fine-grained cluster structure knowledge in the source and target domains, 2) how to effectively incorporate these structure knowledge for adaptation.Regarding the first point, we are motivated by structural domain similarity assumption and propose structural representation learning, which is achieved by enforcing structural consistency between the source and target domains while retaining their individual discriminative properties. Regarding the second point, we firstly devise a novel structural centroid-based label prediction method, which explicitly models structural representations to form discriminative source and target cluster centroids, and estimates the label distribution of each target sample through the cosine similarity between its corresponding target cluster centroid and all the other source cluster centroids. Then, we adopt clustering learning to incorporate these discriminative structure knowledge for adaptation by minimizing the KL divergence between the predictive target label distribution and an introduced auxiliary one. Comprehensive experiments and analyses on four benchmark datasets demonstrate the superiority of the proposed discriminative clustering framework.