In adversarial training, it is important to utilize the limited expressive ability of the model to enhance the learning of vulnerable data points. Conventional adversarial training treats all adversarial data equally in the loss function, which may result in unreasonable allocation of the model’s expressive ability, limiting further robustness enhancement. We propose a dynamic weighting loss method based on robust distance to adjust the decision boundary of the model at data points. It enables the model to prioritize the robustness improvement of the more vulnerable data points in adversarial training. The method quantifies the robustness of the model over data points by robust distances, defining relative distances as adjusted expectations of the model’s decision boundaries at the data points and calculating the adversarial learning priorities for the data points. Experimental results on multiple datasets show that the method can further improve the robustness of the model.

Soft robots and smart materials have seen rapid advancements in recent years, with significant potential applications in medical devices. Liquid crystal elastomers (LCEs) exhibit unique attributes of large deformations and diverse actuation modes, facilitating controllable bending in soft medical catheters and thereby enhancing their maneuverability during medical procedures. However, LCEs exhibit strong hysteresis, which makes their modeling and control challenging. In this paper, we develop a dynamic model of a light-stimulated LCE to describe its nonlinear time-dependent behavior. We first derive the relationship between the input laser power and the resulting temperature change of the LCE actuator, and then analyze the viscoelastic behavior by taking advantage of a spring-dashpot frame. For both the linear contraction actuator and the bending actuator, the dynamic equations can describe their behavior with acceptable errors. In the future, we will further test the LCE-based bending actuator of optimal design, and then perform real-time control of soft catheters with assistance of LCE actuators.

ABSTRACT Image‐text retrieval faces challenges such as insufficient fusion of cross‐modal matching relationships, limited ability to distinguish between positive and negative samples, and inadequate feature representation. To address these issues, this paper proposes a new visual‐semantic interaction network (CFISRO). Building upon standard feature extractors, the network jointly models image and text features through a multi‐layer cross‐interaction module, capturing the rich semantic relationships between images and text. During the training process, a positive–negative sample similarity ranking optimization strategy is introduced to effectively reduce the distance between matching sample pairs while expanding the distance between non‐matching samples, enhancing the model's ability to distinguish. Furthermore, the network optimizes the image‐text matching classification performance by minimizing the difference between the predicted probability distribution and the true label distribution. Overall, CFISRO builds a collaborative optimization mechanism across multiple levels of image‐text representation learning, similarity modeling, and matching discrimination, significantly improving the representation quality and matching accuracy in cross‐modal retrieval tasks. Experimental results show that our method achieves excellent performance on benchmark datasets such as MS‐COCO and Flickr30K.

As 3D scanning devices and depth sensors advance, point clouds have attracted increasing attention as a format for 3D representation. Nevertheless, the tremendous amount of data in point clouds significantly burden transmission and storage. To address these problems, we propose a multi-scale end-to-end framework for point cloud geometry compression. Firstly, point transformer is used to extract the global feature of geometry information, embedding the geometry information and the relation among points. Secondly, the multi-scale neighbor embedding strategy is used to extract the level of details within multi-scale and multi-resolution feature of point clouds. Finally, to reconstruct the point cloud with high quality in the decoder, the local spatial information is restored via graph spatial extension based on local down-sampled features and global features. Experimental results show that we achieve around 34% bit rate reduction on average over competitive point cloud geometry compression methods.

As 3D scanning devices and depth sensors advance, dynamic point clouds have attracted increasing attention as a format for 3D objects in motion, with applications in various fields such as immersive telepresence, navigation for autonomous driving and gaming. Nevertheless, the tremendous amount of data in dynamic point clouds significantly burden transmission and storage. To this end, we propose a complete compression framework for attributes of 3D dynamic point clouds, focusing on optimal inter-coding. Firstly, we derive the optimal inter-prediction and predictive transform coding assuming the Gaussian Markov Random Field model with respect to a spatio-temporal graph underlying the attributes of dynamic point clouds. The optimal predictive transform proves to be the Generalized Graph Fourier Transform in terms of spatio-temporal decorrelation. Secondly, we propose refined motion estimation via efficient registration prior to inter-prediction, which searches the temporal correspondence between adjacent frames of irregular point clouds. Finally, we present a complete framework based on the optimal inter-coding and our previously proposed intra-coding, where we determine the optimal coding mode from rate-distortion optimization with the proposed offline-trained λ-Q model. Experimental results show that we achieve around 17% bit rate reduction on average over competitive dynamic point cloud compression methods.

Point cloud has attracted more and more attention in 3D object representation, especially in free-view rendering. However, it is challenging to efficiently deploy the point cloud due to its huge data amount with multiple attributes including coordinates, normal and color. In order to represent point clouds more compactly, we propose a novel point cloud compression method for attributes, based on geometric clustering and Normal Weighted Graph Fourier Transform (NWGFT). Firstly, we divide the entire point cloud into different sub-clouds via K-means based on the geometry to acquire sub-clouds with more uniform structures, which enables efficient representation with less cost. Secondly, for the purpose of reducing the redundancy further, we apply NWGFT to each sub-cloud, in which graph edge weights are derived from the similarity in normal. Finally, extensive experimental results show that, compared with traditional transform based point cloud compression, the proposed approach achieves about 34.34% bit rate reduction on average for Y components of color.

We propose an adaptive scanning scheme to improve color attribute compression performance of static point cloud. To better exploit the intrinsic correlations of point cloud color data, multiple scanning modes are dedicated to projecting the color attributes into a series of texture blocks before compression. The best mode is subsequently selected in the sense of rate-distortion optimization, such that the compression flexibility and performance can be greatly improved. To achieve rate-distortion optimized scan, the Lagrange multiplier that balances the rate and distortion is off-line derived based on the statistics of color attribute coding. The proposed algorithm is implemented in reference software for point cloud compression (PCC-MP3DG), which is introduced by 3D Graphics (3DG) group of Motion Picture Experts Group (MPEG). Experimental results show that proposed method performs significantly better than the PCC-MP3DG reference software, and on average 6.51% and 9.36% bit-rate savings can be achieved for high and low bit rate codings, respectively.

In this paper, we introduce Cellular Automata-a dynamic evolution model to intuitively detect the salient object. First, we construct a background-based map using color and space contrast with the clustered boundary seeds. Then, a novel propagation mechanism dependent on Cellular Automata is proposed to exploit the intrinsic relevance of similar regions through interactions with neighbors. Impact factor matrix and coherence matrix are constructed to balance the influential power towards each cell's next state. The saliency values of all cells will be renovated simultaneously according to the proposed updating rule. It's surprising to find out that parallel evolution can improve all the existing methods to a similar level regardless of their original results. Finally, we present an integration algorithm in the Bayesian framework to take advantage of multiple saliency maps. Extensive experiments on six public datasets demonstrate that the proposed algorithm outperforms state-of-the-art methods.