Mineralization of Biomaterials for Bone Tissue Engineering

Mineralized biomaterials have been demonstrated to enhance bone regeneration compared to their non-mineralized analogs. As non-mineralized scaffolds do not perform as well as mineralized scaffolds in terms of their mechanical and surface properties, osteoconductivity and osteoinductivity, mineralization strategies are promising methods in the development of functional biomimetic bone scaffolds. In particular, the mineralization of three-dimensional (3D) scaffolds has become a promising approach for guided bone regeneration. In this paper, we review the major approaches used for mineralizing tissue engineering constructs. The resulting scaffolds provide minerals chemically similar to the inorganic component of natural bone, carbonated apatite, Ca5(PO4,CO3)3(OH). In addition, we discuss the characterization techniques that are used to characterize the mineralized scaffolds, such as the degree of mineralization, surface characteristics, mechanical properties of the scaffolds, and the chemical composition of the deposited minerals. In vitro cell culture studies show that the mineralized scaffolds are highly osteoinductive. We also summarize, based on literature examples, the applications of 3D mineralized constructs, as well as the rationale behind their use. The mineralized scaffolds have improved bone regeneration in animal models due to the enhanced mechanical properties and cell recruitment capability making them a preferable option for bone tissue engineering over non-mineralized scaffolds

SigFormer: Sparse Signal-Guided Transformer for Multi-Modal Human Action Segmentation

Multi-modal human action segmentation is a critical and challenging task with a wide range of applications. Nowadays, the majority of approaches concentrate on the fusion of dense signals (i.e., RGB, optical flow, and depth maps). However, the potential contributions of sparse IoT sensor signals, which can be crucial for achieving accurate recognition, have not been fully explored. To make up for this, we introduce a Sparse signalguided Transformer (SigFormer) to combine both dense and sparse signals. We employ mask attention to fuse localized features by constraining cross-attention within the regions where sparse signals are valid. However, since sparse signals are discrete, they lack sufficient information about the temporal action boundaries. Therefore, in SigFormer, we propose to emphasize the boundary information at two stages to alleviate this problem. In the first feature extraction stage, we introduce an intermediate bottleneck module to jointly learn both category and boundary features of each dense modality through the inner loss functions. After the fusion of dense modalities and sparse signals, we then devise a two-branch architecture that explicitly models the interrelationship between action category and temporal boundary. Experimental results demonstrate that SigFormer outperforms the state-of-the-art approaches on a multi-modal action segmentation dataset from real industrial environments, reaching an outstanding F1 score of 0.958. The codes and pre-trained models have been available at https://github.com/LIUQI-creat/SigFormer

Part-level Action Parsing via a Pose-guided Coarse-to-Fine Framework

Action recognition from videos, i.e., classifying a video into one of the pre-defined action types, has been a popular topic in the communities of artificial intelligence, multimedia, and signal processing. However, existing methods usually consider an input video as a whole and learn models, e.g., Convolutional Neural Networks (CNNs), with coarse video-level class labels. These methods can only output an action class for the video, but cannot provide fine-grained and explainable cues to answer why the video shows a specific action. Therefore, researchers start to focus on a new task, Part-level Action Parsing (PAP), which aims to not only predict the video-level action but also recognize the frame-level fine-grained actions or interactions of body parts for each person in the video. To this end, we propose a coarse-to-fine framework for this challenging task. In particular, our framework first predicts the video-level class of the input video, then localizes the body parts and predicts the part-level action. Moreover, to balance the accuracy and computation in part-level action parsing, we propose to recognize the part-level actions by segment-level features. Furthermore, to overcome the ambiguity of body parts, we propose a pose-guided positional embedding method to accurately localize body parts. Through comprehensive experiments on a large-scale dataset, i.e., Kinetics-TPS, our framework achieves state-of-the-art performance and outperforms existing methods over a 31.10% ROC score.Comment: Accepted by IEEE ISCAS 2022, 5 pages, 2 figures. arXiv admin note: text overlap with arXiv:2110.0336

Parsing is All You Need for Accurate Gait Recognition in the Wild

Binary silhouettes and keypoint-based skeletons have dominated human gait recognition studies for decades since they are easy to extract from video frames. Despite their success in gait recognition for in-the-lab environments, they usually fail in real-world scenarios due to their low information entropy for gait representations. To achieve accurate gait recognition in the wild, this paper presents a novel gait representation, named Gait Parsing Sequence (GPS). GPSs are sequences of fine-grained human segmentation, i.e., human parsing, extracted from video frames, so they have much higher information entropy to encode the shapes and dynamics of fine-grained human parts during walking. Moreover, to effectively explore the capability of the GPS representation, we propose a novel human parsing-based gait recognition framework, named ParsingGait. ParsingGait contains a Convolutional Neural Network (CNN)-based backbone and two light-weighted heads. The first head extracts global semantic features from GPSs, while the other one learns mutual information of part-level features through Graph Convolutional Networks to model the detailed dynamics of human walking. Furthermore, due to the lack of suitable datasets, we build the first parsing-based dataset for gait recognition in the wild, named Gait3D-Parsing, by extending the large-scale and challenging Gait3D dataset. Based on Gait3D-Parsing, we comprehensively evaluate our method and existing gait recognition methods. The experimental results show a significant improvement in accuracy brought by the GPS representation and the superiority of ParsingGait. The code and dataset are available at https://gait3d.github.io/gait3d-parsing-hp .Comment: 16 pages, 14 figures, ACM MM 2023 accepted, project page: https://gait3d.github.io/gait3d-parsing-h