Semi-supervised learning (SSL) enhances model generalizability by jointly leveraging labeled and unlabeled data. Nevertheless, the closed-world assumption of SSL always fails in open-world scenarios, where unlabeled data often contains novel classes. To address this limitation, open-world SSL (OWSSL) has been proposed as a more realistic paradigm, aiming not only to recognize known classes but also to discover novel classes. Existing OWSSL methods typically rely on representation similarity and pseudo-labeling to discriminate classes. However, during model training, these methods neglect the inherent class-prediction bias, consequently leading to self-reinforcing confirmation bias in pseudo-labels and representation confusion for hard novel classes. To address these critical challenges, we propose a Open-world Bias-guided Pseudo-label Refinement approach, named OpenBPR, which is the first to regard class prediction bias as the reference to guide the debiased pseudo-labeling and class representation decoupling. In OpenBPR, we propose a debiased pseudo-labeling method based on expectation-maximization, which exploits class prediction bias to dynamically optimize pseudo-labels, effectively alleviating confirmation bias in pseudo-labels. Furthermore, we propose a class-aware representation decoupling strategy for hard novel classes, which decouples representations by the designed competitive class decoupling regularization to assist in improving the refinement performance of pseudo-labels. Experimental results on a series of benchmark datasets demonstrate that OpenBPR outperforms state-of-the-art methods in discriminating both known and novel classes.

Weakly-supervised fine-grained human parsing, which decomposes the human body into several parts and various fashion items only with some easier labels, poses a more challenging visual task and cannot be well solved by general weakly-supervised approaches. In this case, we first explore the feasibility of utilizing point-level labels to address this task. Toward this, we propose the prior-structure driven weakly-supervised learning for fine-grained human parsing. Following previous practices, we design a pseudo label initialization mechanism to produce high-quality pixel-level pseudo labels by utilizing the powerful image segmentation model Segment Anything Model (SAM). Then we propose the Feature Propagation based on Prior-Structure (FPPS) module which formalizes prior-structure knowledge as an adjacency matrix constructed from superpixel and emploies a learnable Graph Neural Network (GNN) as the feature propagator. FPPS can optimize the features of unlabeled pixels to enhance the weakly-supervised learning. The framework further designs the Refinement Pseudo Label (RPL) strategy to generate denser supervision from past sub-optimal models. To the best knowledge, this work is the first attempt to perform fine-grained human parsing in a weakly-supervised manner. We conduct extensive experiments on two challenging fine-grained datasets, including ATR and LIP. Experimental results show that the proposed weakly-supervised method yields a comparable result to strongly-supervised methods and even outperforms other state-of-the-art approaches in semi-supervised human parsing tasks.

Assembly sequence planning (ASP) is the most important process in the product lifecycle. This paper aims to propose a parallel assembly sequence planning method based on sparrow search algorithm (SSA) to improve the assembly quality and efficiency. The proposed method divides the problem into assembly unit division (AUD) part and ASP part. In AUD part, we use the markov clustering algorithm (MCA) to solve the AUD problem. In ASP part, the ASP evaluation system is designed for the assembly part sequence planning (APSP) problem and assembly unit sequence planning (AUSP) problem, and the discrete artificial sparrow search algorithm (DASSA) is proposed for solving the discrete APSP problem. The results of comparison experiment verify the effectiveness of the proposed method. Compared with genetic algorithm (GA) and particle swarm optimization (PSO), the proposed DASSA gets the optimal results and fastest convergence. The method proposed solves ASP problem to efficiently perform parallel assembly sequence planning and reduce production time, making it cost-effective in intelligent manufacturing.

Optical Communication Simulation Task Allocation (OCSTA) constitutes an interdisciplinary quandary. This paper proposes a swarm intelligence approach harnessing a Discrete Dung Beetle Optimizer (DDBO) algorithm with a globally equilibrated strategy to offer a resolution avenue for this intricate conundrum. Firstly, we provide a comprehensive exposition of the mathematical model underpinning the OCSTA, which employs the spatial positioning of the population to articulate diverse allocation solutions. Then, a weighted random selection technique is used to generate initial solutions. Thirdly, a predator avoidance strategy is introduced to facilitate updates in the dung beetle position. Finally, the global equilibrium mechanism with the swap operator is exploited to harmonize the exploratory and exploitative capabilities, thereby further augmenting the quality of the solutions. We conducted several simulation experiments1across various distinct task load scenarios, and statistical tests are employed to evaluate the significant differences between the proposed algorithm and other state-of-the-art methods. The outcomes revealed that the DDBO solution yielded an improvement of approximately 15.8%, which underscores the competitiveness and robustness of solving the OCSTA.

Directed Acyclic Graphs (DAGs) are widely utilized across various domains for tasks such as graph algorithms, data flow analysis, program optimization, and machine learning. Representing DAGs using General-purpose Programming Languages (GPLs) or Data Serialization Formats (DSFs) can lead to complex and obscure expressions, making it challenging to comprehend and manage the codebase. Domain-Specific Languages (DSLs) offer a more tailored approach, but come with limitations and development overhead. This paper introduces the Open Customizable Modeling Language (OpenCML), a universal DAG modeling language specification that aims to provide a standardized and customizable framework for modeling and scripting DAGs. Evaluations demonstrate that OpenCML offers expressive power, customizability, and interoperability, simplifying the learning process and providing a powerful solution for DAG modeling and scripting.

Surface defects of railway trackside equipment pose a serious risk on the safety of railway transportation systems. Image-based surface defect detection methods have made significant progress. However, the image background of trackside equipment is complex, and there is a large amount of noise, which makes existing methods inadequate in accurately detecting small surface defect regions. To tackle with this issue, we propose a coarse-to-fine parallelizable surface defect detection approach to hierarchically detect the defects of trackside equipment. Firstly, a detection network is designed to locate and extract trackside equipment, which aims at roughly focusing the detection field from the original image to the region of interest of individual trackside equipment. Then, a novel semantic segmentation network is proposed to segment the major components of trackside equipment, so as to further finely focus on the defect regions. We apply multiple segmentation networks to parallelly segment various trackside equipment. In the segmentation network, a dense feature enhancement method is introduced to strengthen the high-level semantic information, and a feature partitioning enhancement strategy is designed to improve the segmentation performance for small defect regions. Finally, according to the visual characteristics of the segmentation output, we propose a defect recognizer to discriminate the defects. Extensive experimental results demonstrate that the proposed surface defect detection approach achieves higher accuracy for trackside equipment.

Long-term time-series forecasting is an extensive research topic and is of great significance in many fields. However, the task of long-term time-series forecasting is accompanied by the problem of increasing cumulative error and decreasing time correlation. To overcome these shortcomings, this article proposes a prediction framework based on the nonlinear fuzzy information granule (NFIG) series, which can boost the long-term performance of most predictors. First, we propose the representation of the NFIG for the first time, replacing the linear core lines with nonlinear time-dependent curves. Second, we propose a temporal window splitting algorithm based on curvature equations and weighted directed graphs, which can not only merge temporal windows with the same trend but also cointegrate incremental data. Finally, the nonlinear trend fuzzy granulation can be employed as a data preprocessing module for various time-series predictors to achieve a better long-term forecasting performance. As a typical time-series forecasting task, the precise long-term forecast of traffic flow data can relieve the overburdened traffic system and improve the traffic environment to a certain extent. Thus, the proposed method is employed for the long-term traffic flow forecasting. Compared with existing forecasting models, which achieves superior performances.

Deep learning technologies have been applied in various computer vision tasks in recent years. However, deep models suffer performance decay when some unforeseen data are contained in the testing dataset. Although data enhancement techniques can alleviate this dilemma, the diversity of real data is too tremendous to simulate. To tackle this challenge, we study a scheme for improving the robustness and efficiency of the deep network training process in visual tasks. Specifically, first, we build positive and negative sample pairs based on a class-sensitive strategy. Then, we construct a feature-consistent learning strategy based on contrastive learning to constrain the representations of interclass features while paying attention to the intraclass features. To extend the effect of the consistent strategy, we propose a novel contrastive Jensen-Shannon divergence consistency loss (JS loss) to restrict the probability distributions of different sample pairs. The proposed scheme successfully enhances the robustness and accuracy of the utilized model. We validated our approach by conducting extensive experiments in the domains of model robustness and few-shot object detection (FSOD). The results showed that the proposed method achieved remarkable gains over state-of-the-art (SOTA) methods. We obtained a 3.2% average improvement over the best-performing FSOD method.

Human pose estimation plays an important role in human action recognition, human-computer interaction, animation. Most existing methods commonly utilize cascaded pyramid or stacked hourglass network to fuse multi-scale feature from different levels, which greatly enhances the performance but brings a tremendous amount of computing, so it is difficult to achieve real-time human pose estimation. In this paper, we propose a newly-designed network named Complementary Feature Pyramid Network (CFPNet) for human pose estimation with a focus on efficient multi-scale feature generate and fusion method. CFPNet extends the range of receptive fields for each network layer with the help of Feature Mix Bottleneck (FMB) block which constructs hierarchical connections and mixes multiple receptive fields features in a single bottleneck block. In order to reduce the redundant gradient information during the network optimization and construct a lightweight network, Cross Stage Partial (CSP) connection is introduced into the CFPNet. Complementary Feature Fusion (CFF) block is proposed, which can adaptively select complementary information from different levels for fusion to maximize the effective feature in the output of CFPNet. Through the above improvements, CFPNet comprises more affluent multi-scale feature and lower model complexity. Especially, CFPNet-101 achieves the 72.3% AP at 31.7 FPS on the MS COCO dataset only with 1.96 GFLOPs and 10.5M Params. Compared with the existing methods, CFPNet has competitive accuracy and can run in real-time.

Abstract Due to the inherent nonlinear nature of data, traditional linear methods have some limitations in finding the intrinsic dimensions of motion capture (Mo‐cap) data. Mo‐cap data are more in line with the characteristics of the manifold. Assuming that the data are initially a low‐dimensional manifold and uniformly sampled in high‐dimensional Euclidean space, manifold learning recovers low‐dimensional manifold structures from high‐dimensional sampled data. This paper proposes an automatic segmentation method based on geodesics by introducing a Riemannian manifold. We convert Mo‐cap data from Euler angles into quaternions, calculate the intrinsic mean of the motion sequence, hemispherize quaternions, and use logarithmic and exponential mapping to calculate geodesic distances instead of quaternions. The experimental results show that the algorithms can achieve automatic segmentation and have a better segmentation effect.

In traditional exemplar-based image inpainting algorithm, the confidence value will rapidly decrease to zero as the inpainting process progresses.As a consequence, it will lead to unreliable result of the priority calculation and wrong direction of the process.In addition, traditional methods usually use the sum of squared differences (SSD) criterion to search the optimal matching block.Since the matching criterion is single and the precision is limited, the process is easy to produce mismatch.In order to solve the above problems, an improved algorithm has been proposed in this paper.First, we proposed a new confidence update algorithm through replacing the previous linear function form by using a logarithmic function form, which can suppress the phenomenon that the confidence attenuation is too fast and improve the accuracy of guiding and inpainting direction.Then, we combine the physical distance between blocks and traditional SSD matching criterion to improve matching accuracy.The experimental results show that the algorithm overcomes the shortcomings of the traditional algorithm and provides higher quality image restoration effects and better visual effects.

Since Correlation Filter appeared in the field of video object tracking, it is great popular due to its excellent performance.The Correlation Filter based tracking algorithms are very competitive in terms of accuracy and speed as well as robustness.However, there are still some fields for improvement in the Correlation Filter based tracking algorithms.First, during the training of the classifier, the background information that can be utilized is very limited.Moreover, the introduction of the cosine window further reduces the background information.These reasons reduce the discriminating power of the classifier.This paper introduces more global background information on the basis of the DCF tracker to improve the discriminating ability of the classifier.Then, in some complex scenes, tracking loss is easy to occur.At this point, the tracker will be treated the background information as the object.To solve this problem, this paper proposes a novel re-detection component.Finally, the current Correlation Filter based tracking algorithms use the linear interpolation model update method, which cannot adapt to the object changes in time.This paper proposes an adaptive model update strategy to improve the robustness of the tracker.

Since Correlation Filter appeared in the field of video object tracking, it is very popular due to its excellent performance. The Correlation Filter-based tracking algorithms are very competitive in terms of accuracy and speed as well as robustness. However, there are still some fields for improvement in the Correlation Filter-based tracking algorithms. First, during the training of the classifier, the background information that can be utilized is very limited. Moreover, the introduction of the cosine window further reduces the background information. These reasons reduce the discriminating power of the classifier. This paper introduces more global background information on the basis of the DCF tracker to improve the discriminating ability of the classifier. Then, in some complex scenes, tracking loss is easy to occur. At this point, the tracker will be treated the background information as the object. To solve this problem, this paper introduces a novel re-detection component. Finally, the current Correlation Filter-based tracking algorithms use the linear interpolation model update method, which cannot adapt to the object changes in time. This paper proposes an adaptive model update strategy to improve the robustness of the tracker. The experimental results on multiple datasets can show that the tracking algorithm proposed in this paper is an excellent algorithm.

Creating realistic virtual humans has been a challenging objective in the areas of computer science research and technology industry. While there are a number of aspects to create realistic virtual humans, this paper focuses on the comprehensive integrated framework of modeling virtual humans with high level autonomy, which aim to reproduce human-like believable behaviors and movements of virtual humans in virtual environment. In the framework, the perception module enables virtual human to explore the virtual environment and gets vision and audition information; the decision networks based behavioral decision-making module allows virtual human react appropriately to the perceived surrounding environment; the hierarchical movement animation control module is designed to generate autonomous character navigation and realistic motions for character animation in virtual environment. The integrated framework presented is tested in the simulated virtual environment.

This paper presents a 3D object classification system based on volumetric parts. As the constituents of 3D object, the parts are described by superquadric-based geons, which enables a more compact 3D object representation. In the developed classification system, the improved interpretation tree method is implemented for classification, where a set of novel integrated features and corresponding constraints are proposed, which not only reflect individual parts’ shape, but model’s topological structure among 3D parts. The constraints are used to define efficient interpretation tree search rules, and the feasible correspondences of unknown object data and the stored models are obtained. Then a similarity measure computation algorithm is proposed to evaluate the shape similarity of the correspondence. The classification system can achieve both whole match and partial match between unknown object data and 3D models with shape similarity ranks; particularly, focus match can be accomplished, in which different key parts may be labeled and all the matched models with corresponding key parts can be obtained. The performance of the presented 3D object classification system is evaluated with a series of experiments.

This paper focuses on the key algorithms and techniques for developing an interactive visual retrieval system for large scale 3D databases, and a novel 3D model retrieval and visualization engine, 3DMIRACLES, has been developed, which integrates effective algorithms and techniques for both shape-based retrieval of 3D models and real-time visualization of the retrieval results in realistic 3D interactive mode. In the retrieval system, interactive visualization for the retrieval user interface and 3D shape retrieval computation are the two most important functional modules. For interactive visualization, a novel 3D viewer has been developed, which implements hybrid rendering method to make much simplification and shortcut processing of 3D rendering computation for achieving high speed and efficient visualization of large scale database; for retrieval computation, new algorithms for 3D shape feature extraction and similarity matching have been developed and implemented.

Proliferation of 3D models necessitates developing efficient methods for indexing or retrieving the models in a large database. Many previous methods for this purpose defined functions on concentric spheres as approximation of 3D geometry for spherical harmonic transform (SHT). In this paper, we point out that this is not robust as the surface of a model may shift between different shells under perturbation, and multi-layer of surfaces may exist in one shell, making the function definition ambiguous. To solve these problems, we propose a method to characterize 3D shape using delta functions. Then, spherical functions are defined by sampling in the frequency domain of the delta functions for SHT. By doing so, our method can support retrieval with controllable acuity, which benefits wider range of applications and facilitates customization to different users. Experiments have shown that our method is more robust than previous approaches.