6 accepted papers
Recently, Large Language Models (LLMs) with in-context learning have demonstrated remarkable potential in handling neural machine translation. However, existing evidence shows that LLMs are prompt-sensitive and it is sub-optimal to apply the fixed prompt to any input for downstream machine translati…
Clustering is a common method used in machine learning to group sample points in a dataset. Gaussian Mixture Clustering (GMC) is a clustering method based on maximum likelihood estimation and expectation maximisation (EM) algorithms. Traditional GMC does not consider the fairness between different s…
In the field of clustering algorithms, the Fuzzy CMeans algorithm stands out for its ability to deal with uncertainty by assigning membership degrees to data points. However, research on the fairness of Fuzzy C-Means algorithms has mainly focused on group fairness, with limited attention to individu…
Speech-preserving facial expression manipulation (SPFEM) aims to modify facial emotions while meticulously maintaining the mouth animation associated with spoken content. Current works depend on inaccessible paired training samples for the person where two aligned frames exhibit the same speech cont…
The capability of video super-resolution (VSR) to synthesize high-resolution (HR) video from ideal datasets has been demonstrated in many works. However, applying the VSR model to real-world video with unknown and complex degradation remains a challenging task. First, existing degradation metrics in…
Lightweight object detection can promote intelligent robotics to recognize surroundings objects with limited computational resources, and thus receives increasing attention in robotics communities. Recently, high-resolution networks (HRNets) can learn high-resolution representation and it obtains ex