8 accepted papers
Rotation invariance remains a core challenge in point cloud analysis, where existing methods often struggle with structural ambiguities and insufficient global context. Most rotation-invariant (RI) representations are derived from local coordinate systems, which inherently suffer from point-pair amb
Recent advances in rotation-invariant (RI) learning for 3D point clouds typically replace raw coordinates with handcrafted RI features to ensure robustness under arbitrary rotations. However, these approaches often suffer from the loss of global pose information, making them incapable of distinguish
Explainable recommendation is crucial for building user trust, yet producing natural-language rationales that faithfully reflect the underlying decision process remains challenging. Most LLM-based explainable recommenders incorporate collaborative signals through shallow prompting or lightweight ada
Speech emotion recognition (SER) hinges on front-ends that expose informative time-frequency (TF) structure from raw speech. Classical short-time Fourier and wavelet transforms impose fixed resolution trade-offs, while prior "superlet" variants rely on integer orders and hand-tuned hyperparameters.…
Multi-modal large language models (MLLMs) have shown remarkable progress in integrating visual and linguistic understanding. Recent efforts have extended these capabilities to 3D understanding through encoder-based architectures that rely on pre-trained 3D encoders to extract geometric features. How
"Semi-supervised learning (SSL) has shown its effectiveness in learning effective 3D representation from a small amount of labelled data while utilizing large unlabelled data. Traditional semi-supervised approaches rely on the fundamental concept of predicting pseudo-labels for unlabelled data and i…
In this paper, a hierarchical Dirichlet process (HDP) mixture model of generalized inverted Dirichlet (GID) distributions with an unsupervised feature selection scheme is developed. The proposed model is learned via a principled variational framework and then deployed for video modeling and segmenta…
An effective novel algorithm to reduce the dimensionality of labeled proportional data is presented which uses an optimal linear projection to project the data into a low-dimensional space. Assuming that each class of the projected data is generated by a mixture of Dirichlet distributions, KL-diverg…