18 accepted papers
High-fidelity reconstruction of mel-spectrograms from EEG signals remains a formidable challenge, primarily due to the inherent inter-subject variability of neural patterns and the semantic gap between heterogeneous feature representations of these two modalities. To alleviate both issues, this pape
Brain visual decoding aims to recognize and reconstruct perceptual visual content from brain activity, providing a promising potential for the development of brain-computer interfaces and brain-inspired intelligence. However, this task faces a fundamental challenge of information asymmetry: while na
Recently, Electroencephalography (EEG) has been increasingly utilized in driving fatigue detection tasks. However, the inter-subject variabilities in EEG data render models trained on one subject ineffective for being directly applied to others. Transfer learning has been widely used to address this…
As a fundamental visual task, optical flow estimation has widespread applications in computer vision. However, it faces significant challenges under adverse lighting conditions, where low texture and noise make accurate optical flow estimation particularly difficult.In this paper, we propose an opti…
Targeting the notorious cumulative drift errors in NeRF SLAM, we propose a Semantic-guided Loop Closure using Shared Latent Code, dubbed SLC<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-f
Visual place recognition (VPR) is essential to many autonomous systems. Existing VPR methods demonstrate attractive performance at the cost of limited generalizability. When deployed in unseen environments, these methods exhibit significant performance drops. Targeting this issue, we present VIPeR,
We present AEGIS-Net, a novel indoor place recognition model that takes in RGB point clouds and generates global place descriptors by aggregating lower-level color, geometry features and higher-level implicit semantic features. However, rather than simple feature concatenation, self-attention module…
Aspect-category-based sentiment analysis (ACSA), which aims to identify aspect categories and predict their sentiments has been intensively studied due to its wide range of NLP applications. Most approaches mainly utilize intrasentential features. However, a review often includes multiple different…
Surface electromyography (sEMG) noninvasively records muscle activities. It provides valuable information about muscle contractions and enables real-time decoding into hand gestures. Recently many studies have successfully demonstrated this capability. However, the accuracy of gesture recognition de…
Aspect-based sentiment analysis (ABSA) has been widely studied since the explosive growth of social networking services. However, the recognition of implicit sentiments that do not contain obvious opinion words remains less explored. In this paper, we propose aspect-category enhanced learning with a…
The heart of multimodal learning research lies the challenge of effectively exploiting fusion representations among multiple modalities.However, existing two-way cross-modality unidirectional attention could only exploit the intermodal interactions from one source to one target modality. This indeed…
Multimodal sentiment analysis is the challenging research area that attends to the fusion of multiple heterogeneous modalities. The main challenge is the occurrence of some missing modalities during the multimodal fusion procedure. However, the existing techniques require all modalities as input, th…
Measuring the sleep quality is important or even crucial for people who are engaged in dangerous jobs such as the high-speed train drivers. Since the scalp EEG data are generated by the neural activities of the brain cortex, it is collected from subjects with different hours of sleep time (4 hours,…
Tensor-based multimodal fusion techniques have exhibited great predictive performance. However, one limitation is that existing approaches only consider bilinear or trilinear pooling, which fails to unleash the complete expressive power of multilinear fusion with restricted orders of interactions. M…
Non-negative matrix factorization (NMF) is an efficient model in learning parts-based data representation. Since the local geometrical structure can be effectively modeled by a nearest neighbor graph, the graph regularized NMF (GNMF) was proposed to make the learned representation more faithfully an…
As one of the matrix factorization models, concept factorization (CF) achieved promising performance in learning data representation in both original feature space and reproducible kernel Hilbert space (RKHS). Based on the consensuses that 1) learning performance of models can be enhanced by exploit…
The central task in graph-based unsupervised feature selection (GUFS) depends on two folds, one is to accurately characterize the geometrical structure of the original feature space with a graph and the other is to make the selected features well preserve such intrinsic structure. Currently, most of…
Non-negative Matrix Factorization (NMF) is a popular model in machine learning, which can learn parts-based representation by seeking for two non-negative matrices whose product can best approximate the original matrix. However, the manifold structure is not considered by NMF and many of the existin…