Current collaborative perception methods often rely on fully annotated datasets, which can be expensive to obtain in practical situations. To reduce annotation costs, some works adopt sparsely supervised learning techniques and generate pseudo labels for the missing instances. However, these methods fail to achieve an optimal confidence threshold that harmonizes the quality and quantity of pseudo labels. To address this issue, we propose an end-to-end Collaborative perception Dual Teacher-Student framework (CoDTS), which employs adaptive complementary learning to produce both high-quality and high-quantity pseudo labels. Specifically, the Main Foreground Mining (MFM) module generates high-quality pseudo labels based on the prediction of the static teacher. Subsequently, the Supplement Foreground Mining (SFM) module ensures a balance between the quality and quantity of pseudo labels by adaptively identifying missing instances based on the prediction of the dynamic teacher. Additionally, the Neighbor Anchor Sampling (NAS) module is incorporated to enhance the representation of pseudo labels. To promote the adaptive complementary learning, we implement a staged training strategy that trains the student and dynamic teacher in a mutually beneficial manner. Extensive experiments demonstrate that the CoDTS effectively ensures an optimal balance of pseudo labels in both quality and quantity, establishing a new state-of-the-art in sparsely supervised collaborative perception.

Periocular recognition is regarded as an alternative trait for biometric recognition that can effectively solve the identification problem under large occlusions. However, few datasets are tailored for periocular recognition. For most compromises, iris datasets at near-infrared wavelengths, miss information about the eyebrows or eyelids. In this paper, a challenging dataset for real scenarios named CASIA-PR-V1 with evaluation protocols is released for periocular recognition. It is collected from multiple types of mobile devices with different resolutions or wavelengths. A rich set of attributes, e.g., ethnicities, is tagged to support fine-grained classification tasks. Moreover, we consider a wide range of noisy data in unconstrained environment, especially for glasses. Superior to its counterparts, this periocular dataset is highly valuable for studying cross-device and cross-spectral periocular recognition with occlusions, as well as fine-grained attribute classification. Additionally, a multiscale disentangled model is proposed to extract discriminating representations for periocular recognition with severe occlusions. Extensive experiments are conducted on CASIA-PR-V1, and the results indicate the superiority of our model for unconstraint periocular recognition.

The Extended Position Based Dynamics (XPBD) approach of Macklin et al. (2016) addresses issues with iteration-dependent behavior in the original Position Based Dynamics (Müller et al., 2007) (PBD). PBD itself is a powerful method for the real-time simulation of elastic objects, however, it is limited in its application to hyperelastic solids. It can only treat models with a strain energy density that is quadratic in some notion of constraint. Furthermore, we show that even when applicable the formulation does not always lead to convergent behaviors with hyperelasticity. We isolate the root cause to be the approximate linearization of the nonlinear backward Euler systems utilized by XPBD. We provide two fixes to these terms that allow for convergent behavior. The first (B-PXPBD) is a small modification to an existing XPBD code, but can only be used with models addressable by the original XPBD. The second (FP-PXPBD) is a more general formulation that extends XPBD (and our residual correction) to arbitrary hyperelasticity. We show that our modifications allow for convergent behavior that rivals accurate techniques like Newton’s method when the computational budget is large without sacrificing the stable and robust behavior exhibited by the original PBD and XPBD when the computational budget is limited.

Abstract The creation of a volumetric mesh representing the interior of an input polygonal mesh is a common requirement in graphics and computational mechanics applications. Most mesh creation techniques assume that the input surface is not self‐intersecting. However, due to numerical and/or user error, input surfaces are commonly self‐intersecting to some degree. The removal of self‐intersection is a burdensome task that complicates workflow and generally slows down the process of creating simulation‐ready digital assets. We present a method for the creation of a volumetric embedding hexahedron mesh from a self‐intersecting input triangle mesh. Our method is designed for efficiency by minimizing use of computationally expensive exact/adaptive precision arithmetic. Although our approach allows for nearly no limit on the degree of self‐intersection in the input surface, our focus is on efficiency in the most common case: many minimal self‐intersections. The embedding hexahedron mesh is created from a uniform background grid and consists of hexahedron elements that are geometrical copies of grid cells. Multiple copies of a single grid cell are used to resolve regions of self‐intersection/overlap. Lastly, we develop a novel topology‐aware embedding mesh coarsening technique to allow for user‐specified mesh resolution as well as a topology‐aware tetrahedralization of the hexahedron mesh.

Group activity analysis has attracted remarkable attention recently due to the widespread applications in security, entertainment and military. This article targets at learning group activity representations with self-supervision, which differs from the majorities relying heavily on manually annotated labels. Moreover, existing Self-Supervised Learning (SSL) methods for videos are sub-optimal to generate such representations because of the complex context dynamics in group activities. In this article, an end-to-end framework termed Contextualized Relation Predictive Model (Con-RPM) is proposed for self-supervised group activity representation learning with predictive coding. It involves the Serial-Parallel Transformer Encoder (SPTrans-Encoder) to model the context of spatial interactions and temporal variations, and the Hybrid Context Transformer Decoder (HConTrans-Decoder) to predict the future spatio-temporal relations guided by holistic scene context. Additionally, to improve the discriminability and consistency of prediction, we introduce a united loss integrating group-wise and person-wise contrastive losses in frame-level as well as the adversarial loss in global sequence-level. Consequently, our Con-RPM learns robust group representations via describing temporal evolutions of individual relationships and scene semantics explicitly. Extensive experimental results on downstream tasks indicate the effectiveness and generalization of our model in self-supervised learning, and present state-of-the-art performance on the Volleyball, Collective Activity, VolleyTactic, and Choi's New datasets.

Position based dynamics [Müller et al. 2007] is a powerful technique for simulating a variety of materials. Its primary strength is its robustness when run with limited computational budget. Even though PBD is based on the projection of static constraints, it does not work well for quasistatic problems. This is particularly relevant since the efficient creation of large data sets of plausible, but not necessarily accurate elastic equilibria is of increasing importance with the emergence of quasistatic neural networks [Bailey et al. 2018; Chentanez et al. 2020; Jin et al. 2022; Luo et al. 2020]. Recent work [Macklin et al. 2016] has shown that PBD can be related to the Gauss-Seidel approximation of a Lagrange multiplier formulation of backward Euler time stepping, where each constraint is solved/projected independently of the others in an iterative fashion. We show that a position-based, rather than constraint-based nonlinear Gauss-Seidel approach resolves a number of issues with PBD, particularly in the quasistatic setting. Our approach retains the essential PBD feature of stable behavior with constrained computational budgets, but also allows for convergent behavior with expanded budgets. We demonstrate the efficacy of our method on a variety of representative hyperelastic problems and show that both successive over relaxation (SOR), Chebyshev and multiresolution-based acceleration can be easily applied.

We present a comprehensive neural network to model the deformation of human soft tissues including muscle, tendon, fat and skin. Our approach provides kinematic and active correctives to linear blend skinning [Magnenat-Thalmann et al. 1989] that enhance the realism of soft tissue deformation at modest computational cost. Our network accounts for deformations induced by changes in the underlying skeletal joint state as well as the active contractile state of relevant muscles. Training is done to approximate quasistatic equilibria produced from physics-based simulation of hyperelastic soft tissues in close contact. We use a layered approach to equilibrium data generation where deformation of muscle is computed first, followed by an inner skin/fascia layer, and lastly a fat layer between the fascia and outer skin. We show that a simple network model which decouples the dependence on skeletal kinematics and muscle activation state can produce compelling behaviors with modest training data burden. Active contraction of muscles is estimated using inverse dynamics where muscle moment arms are accurately predicted using the neural network to model kinematic musculotendon geometry. Results demonstrate the ability to accurately replicate compelling musculoskeletal and skin deformation behaviors over a representative range of motions, including the effects of added weights in body building motions.

The Extended Position Based Dynamics (XPBD) approach of Macklin et al. [2016] addresses the issues with iteration-dependent behavior in the original Position Based Dynamics [2007] (PBD) which itself is a powerful method for the real-time simulation of elastic objects. However, it is limited in its application to hyperelastic solids. It can only treat models with a strain energy density that is quadratic in some notion of constraint. Furthermore, we show that even when applicable the formulation does not always lead to convergent behaviors with hyperelasticity. We isolate the root cause in the approximate linearization of the nonlinear backward Euler systems utilized by XPBD. We provide two fixes to these terms that allow for convergent behavior. The first (B-PXPBD) is a small modification to an existing XPBD code, but can only be used with models addressable by the original XPBD. The second (FP-PXPBD) is a more general formulation that extends XPBD (and our residual correction) to arbitrary hyperelasticity. We show that our modifications allow for convergent behavior that rivals accurate techniques like Newton’s method when the computational budget is large without sacrificing the stable and robust behavior exhibited by the original PBD and XPBD when the computational budget is limited.

Collaborative 3D object detection, with its improved interaction advantage among multiple agents, has been widely explored in autonomous driving. However, existing collaborative 3D object detectors in a fully supervised paradigm heavily rely on large-scale annotated 3D bounding boxes, which is labor-intensive and time-consuming. To tackle this issue, we propose a sparsely supervised collaborative 3D object detection framework SSC3OD, which only requires each agent to randomly label one object in the scene. Specifically, this model consists of two novel components, i.e., the pillar-based masked autoencoder (Pillar-MAE) and the instance mining module. The Pillar-MAE module aims to reason over high-level semantics in a self-supervised manner, and the instance mining module generates high-quality pseudo labels for collaborative detectors online. By introducing these simple yet effective mechanisms, the proposed SSC3OD can alleviate the adverse impacts of incomplete annotations. We generate sparse labels based on collaborative perception datasets to evaluate our method. Extensive experiments on three large-scale datasets reveal that our proposed SSC3OD can effectively improve the performance of sparsely supervised collaborative 3D object detectors.

In this paper, we aim to devise a new framework to compel the network to be equipped with the capability of detecting objects using image-level class labels as supervision. The challenge of such a weakly supervised setting mainly lies in how to make the network accurately understand both semantics and objectness of a given proposal without bounding box annotations. To this end, we contribute a concise framework, named Class Prototypical Network (CPNet). Concretely, our CPNet defines a set of learnable class prototypes to help classify object proposals. To endow the prototypes be not only discriminative for classes but also sensitive for proposals' objectness, we conduct both class-aware cross-attention and location-aware cross-attention between the feature embeddings of the learnable prototypes and the proposals. The learned attention scores are then used to form the proposal-level category information into the image-level one, making the entire framework be trained without any bounding box annotations. Besides, by applying these two kinds of attention mechanisms, the knowledge from both proposals' location and its class information can be successfully transferred into the corresponding prototypes. With the help of prototypes, our CPNet detects true positive object proposals. In addition, the CPNet further introduces a multi-head detection head to perform complementary training, preventing the model from falling into local discriminative parts and improving the model's performance on challenging non-rigid categories. We examine our CPNet on popular benchmarks,i.e., PASCAL VOC 2007, 2012 and MS COCO 2014. Extensive experiments show our CPNet is a simple and effective framework.

Automatic wireless spectrum anomaly detection is vital to intelligent management of electromagnetic spectrum, which aims to detect various jamming and anomalous working states, especially intentional jamming. The intentional jamming has evolved in a variety of ways, but the existing spectrum anomaly detection efforts give little consideration to the diverse intentional jamming. Here, we firstly generate a rich dataset consisting of five types of normal signals and four types of intentional jamming. In order to effectively detect anomalies, we propose AWGAN, a novel anomaly detection method based on Wasserstein generative adversarial network. AWGAN can not only learn the distribution of normal time-frequency waterfall images in a latent space, but also remember the detailed features of normal images, and generate same images as the normal images by adversarial training. To detect anomalies, we propose a random reverse mapping (RRM) method based on backpropagation, to map a new time-frequency waterfall image into the latent space, so as to find the vector closest to the distribution of the new image in the latent space. We also define a scoring criterion to score images indicating their fit into the learned distribution. The experimental results show that the comprehensive detection ability of our method is superior to other methods for detecting the four types of anomalies.

Imputation for missing data is an important task of data mining, which may influence the data mining result. In this paper, Missing Categorical Data Imputation Based on Similarity (MIBOS) is proposed to solve this problem. The algorithm defines a similarity model between objects with incomplete data, constructing the similarity matrix of objects and further gets the nearest undifferentiated object sets of each object to impute the missing data iteratively. In the imputing process, the imputed value will be directly applied to the same iteration and the following iterations. Experiments with three UCI benchmark data sets show the improvement of the proposed algorithm from perspectives of complete rate, accuracy and time efficiency.