17 accepted papers
Proactive intention decoding remains a critical yet underexplored challenge in brain–machine interfaces (BMIs), especially under naturalistic, self-initiated behavior. Existing systems rely on reactive decoding of motor cortex signals, resulting in substantial latency. To address this, we leverage t
This paper presents a novel event-based depth sensing system with line laser scan. Our main contribution involves both hardware and software improvements to previous state-of-the-art works. The polygon mirror scanner is designed to steer line laser with a constant velocity, which minimizes non-linea
This paper focuses on the basic structure of Chinese characters: semantic-phonetic compound characters. This paper takes advantage of this feature of Chinese characters and innovatively proposes a Chinese speech-processing method based on character shape. We use the association between the character…
Existing multimodal models typically assume the availability of all modalities, leading to significant performance degradation when certain modalities are missing. Recent methods have introduced prompt learning to adapt pretrained models to incomplete data, achieving remarkable performance when the
Audio-visual speech recognition (AVSR) aims to enhance the robustness of an automatic speech recognition (ASR) systems by incorporating visual information from lip movements, especially in challenging noisy environments. Nevertheless, most current approaches either involve training from scratch or f…
The utilization of multimodal cues enhances the effectiveness of specific cognitive tasks in audio-visual learning. However, on the one hand, designing a unified model for multimodal learning poses challenges due to the presence of information redundancy and modality noise. On the other hand, existi…
Dataset condensation aims to synthesize compact yet informative datasets that retain the training efficacy of full-scale data, offering substantial gains in efficiency. Recent studies reveal that the condensation process can be vulnerable to backdoor attacks, where malicious triggers are injected in…
Convolutional neural networks (ConvNets) with large effective receptive field (ERF), still in their early stages, have demonstrated promising effectiveness while constrained by high parameters and FLOPs costs and disrupted asymptotically Gaussian distribution (AGD) of ERF. This paper proposes an alt…
Object recognition in open environments, e.g., video surveillance, poses significant challenges due to the inclusion of unknown and multi-granularity tasks (MGT). However, recent methods exhibit limitations as they struggle to capture subtle differences between different parts within an object and a…
Federated Learning (FL) heavily depends on label quality for its performance. However, the label distribution among individual clients is always both noisy and heterogeneous. The high loss incurred by client-specific samples in heterogeneous label noise poses challenges for distinguishing between cl…
We introduce Ground-Fusion, a low-cost sensor fusion simultaneous localization and mapping (SLAM) system for ground vehicles. Our system features efficient initialization, effective sensor anomaly detection and handling, real-time dense color mapping, and robust localization in diverse environments.…
Accurate and dense depth estimation with stereo cameras and LiDAR is an important task for automatic driving and robotic perception. While sparse hints from LiDAR points have improved cost aggregation in stereo matching, their effectiveness is limited by the low density and non-uniform distribution.…
Conventional Federated Domain Adaptation (FDA) approaches usually demand an abundance of assumptions, which makes them significantly less feasible for real-world situations and introduces security hazards. This paper relaxes the assumptions from previous FDAs and studies a more practical scenario na…
We introduce a lightweight network to improve descriptors of keypoints within the same image. The network takes the original descriptors and the geometric properties of keypoints as the input, and uses an MLP-based self-boosting stage and a Transformer-based cross-boosting stage to enhance the descr…
In this paper, we propose KGenre, a knowledge-embedded music representation learning framework for improved genre classification. We construct the knowledge graph from the metadata in the open-source FMA-medium and OpenMIC-2018 datasets, with no extra information/effort required. KGenre then mines t…
In this paper, a novel navigation learning method in continuous action space among crowds based on relational graph is proposed which can be directly deployed on differential-drive mobile robots without any change. More specifically, in order to increase generalization ability in crowd sizes, Graph…
Due to the head swinging and the body winding, the self-localization and path following for snake-like robots based on vision are very challenging. In this paper, a novel pantilt compensation method and curve parameter compensation path following controller are proposed to solve these problems, whic…