Chinese Ink Cartoon (CIC) generation is challenging, due to the lack of paired photo-cartoon data and the severe geometric deformations between photos and cartoons. To tackle this challenge, in this paper, we build a high-quality CIC dataset with rich annotations, and propose a novel CIC generation method based on Multiscale Semantic Consistency (MuSeC). The CIC dataset consists of $\mathbf{1, 2 0 0}$ high-resolution CIC paintings with nearly 2,000 annotations of human faces and bodies, respectively. The generation task is formulated as an unsupervised image-to-image translation problem. First, we constrain the generated cartoon to pixel-wisely convey the semantic structure of the input photo through a learned CIC face parsing network. Additionally, we use patch-wise contrastive learning and global semantic consistency loss. In this way, the generated cartoon is optimized to precisely present the identity, attributes and structure of the input photo. Experimental results show that the proposed method can generate high-quality CICs, and outperforms previous methods both quantitatively and qualitatively. Our dataset and code have been released at github.com/AiArt-Gao/MuSeC

In this paper, we present a novel system for Human-Robot Interactive Creation of Artworks (HRICA). Different from previous robot painters, HRICA allows a human user and a robot to alternately draw strokes on a canvas, to collaboratively create a portrait drawing through frequent interactions. The key is to enable the robot to understand human intentions, during the interactive creation process. We here formulate this as a mask-free image inpainting problem, and propose a novel method to estimate the complete version of a portrait drawing, after the human user has drawn some initial strokes. In this way, the robot can select some complementary strokes and draw them on the canvas. To train and evaluate our inpainting method, we construct a novel large-scale portrait drawing dataset, CelebLine, which composes of high-quality portrait line-drawings, with dense labels of both 2D semantic parsing masks and 3D depth maps. Finally, we develop a human-robot interactive drawing system with low-cost hardware, user-friendly interface, and interesting creation experience. Experiments show that our robot can stably cooperate with human users to create diverse styles of portrait drawings. In addition, our portrait drawing inpainting method significantly outperforms previous advanced methods. The code and dataset have been released at: https://github.com/fei-aiart/HRICA.

With the rise of edge devices, video analytics has become a key application in edge computing. Single-camera systems are limited by their Field of View (FoV), making them inadequate for complex environments such as traffic intersections. In this paper, we propose Adaptive Cross-Camera Video Analytics (ACCVA), a novel system for intelligent traffic monitoring. ACCVA introduces a novel camera selection algorithm that adaptively chooses the best camera based on vehicle location and historical data, as well as an adaptive retention algorithm to prevent occlusion and recover lost objects. ACCVA establishes dynamic segmentation of camera regions and cross-camera correlation, managing real-time video inference and result sharing. Implemented on the NVIDIA Jetson Orin NX and evaluated with a real-world traffic surveillance dataset, ACCVA significantly reduces end-to-end latency and enhances accuracy compared to existing state-of-the-art systems. ACCVA excels in complex scenarios by balancing low latency and high accuracy, providing an effective solution for intelligent traffic monitoring.

The research on community structure is a key to analyze the network functionality and topology, and thus it is significant to detect and analysis the community structure. During the abstract process from an actual system to a network, especially for a large-scale network, it is inevitable to have mistaken connections between nodes or have connection missing. In addition, in real applications, from time to time we can obtain prior information in the form of pairwise constraints between nodes besides topology information, although they may be inaccurate or conflicted. These noises in the network-related information will dramatically reduce the accuracy of community detection. Hence, in this paper, we introduce a dissimilarity index to determine the trustworthiness of pairwise constraints and settle the conflict of pairwise constraints. Then, focusing on the community detection with false connections or conflicted connections, we propose a pairwise constrained structure-enhanced extremal optimization-based semi-supervised algorithm (PCSEO-SS algorithm). Compared with existing semi-supervised community detection approaches, the experimental results executed on real networks and synthetic networks, show that PCSEO-SS can solve the problem of false connections or conflicted connections to some extent and detect the community structure more precisely.