13 accepted papers
This paper presents HyperGraph ROS, an open-source robot operating system that unifies intra-process, inter-process, and cross-device computation into a computational hypergraph for efficient message passing and parallel execution. In order to optimize communication, HyperGraph ROS dynamically selec
Medical imaging faces significant challenges in single-domain generalization (SDG) due to the diversity of imaging devices and the variability among data collection centers. To address these challenges, we propose \textbf{TinyMIG}, a framework designed to transfer generalization capabilities from vi…
Previous research on the diagnosis of Bipolar disorder has mainly focused on resting-state functional magnetic resonance imaging. However, their accuracy can not meet the requirements of clinical diagnosis. Efficient multimodal fusion strategies have great potential for applications in multimodal da…
Consumer robots can accompany children growing up, improving their abilities while playing and entertaining. This paper presents an open-source, practical, low-cost robotic Chinese chess player. The proposed system includes an elaborate mechanical structure, a simple kinematic solution, a novel robo…
With the rapid development of vision-based deep learning (DL), it is an effective method to generate large-scale synthetic data to supplement real data to train the DL models for domain adaptation. However, previous vanilla domain adaptation methods generally assume the same label space, and such an…
Semantic segmentation is of great value to autonomous driving and many robotic applications, while it highly depends on costly and time-consuming pixel-level annotation. To make full use of unlabeled data, this work proposes a deep tri-training framework (dubbed DTT) to utilize labeled along with un
Hand pose estimation constitutes prime attainment for human-machine interaction-based applications. Real-time operation is vital in such tasks. Thus, a reliable estimator should exhibit low computational complexity and high precision at the same time. Previous works have explored the regression tech…
Most previous progress in object tracking is realized in daytime scenes with favorable illumination. State-of-the-arts can hardly carry on their superiority at night so far, thereby considerably blocking the broadening of visual tracking-related unmanned aerial vehicle (UAV) applications. To realize
The analysis of facial expression is a very complex and challenging problem. Most researches for automated Facial Expression Recognition (FER) are mainly based on deep learning networks, rarely considering data imbalance. This paper commits to addressing the long-tail distribution problems among lar…
Estimating a scene’s depth to achieve collision avoidance against moving pedestrians is a crucial and fundamental problem in the robotic field. This paper proposes a novel, low complexity network architecture for fast and accurate human depth estimation and segmentation in indoor environments, aimin…
In this paper, we propose a vanishing point aided LiDAR-Visual-Inertial estimator to achieve real-time, low-drift and robust pose estimation. The proposed method is mainly composed of 3 sequential modules, namely IMU-aided vanishing point (VP) detection module, voxel-map based feature depth associat…
Few-shot learning, aiming to learn novel concepts from few labeled examples, is an interesting and very challenging problem with many practical advantages. To accomplish this task, one should concentrate on revealing the accurate relations of the support-query pairs. We propose a transductive relati…
Visual loop closure detection, which can be considered as an image retrieval task, is an important problem in SLAM (Simultaneous Localization and Mapping) systems. The frequently used bag-of-words (BoW) models can achieve high precision and moderate recall. However, the requirement for lower time co…