5 accepted papers
While Vision-Language Models (VLMs) have achieved remarkable performance across diverse downstream tasks, recent studies have shown that they can inherit social biases from the training data and further propagate them into downstream applications. To address this issue, various debiasing approaches
Interpretable methods such as Vision–Language Models (VLMs) have advanced affect analysis by aligning images with textual descriptions. However, relying on text as an intermediate proxy faces critical limitations: linguistic templates are inherently discrete, making them fundamentally incompatible w…
Domain generalization (DG) and algorithmic fairness are two key challenges in machine learning. However, most DG methods focus solely on minimizing expected risk in the unseen target domain, without considering algorithmic fairness. Conversely, fairness methods typically do not account for domain sh
Emotion recognition in conversations (ERC) has gained more attention, where contextual information modeling and multimodal fusion have been the focus and challenges in recent years. In this paper, we proposed a Multi-Scale Receptive Field Graph model (MSRFG) to tackle the challenges of ERC. Specific…
Basketball shot location data provide valuable summary information regarding players to coaches, sports analysts, fans, statisticians, as well as players themselves. Represented by spatial points, such data are naturally analyzed with spatial point process models. We present a novel nonparametric Ba…