3 papers
cs.CL2026
Injecting Distributional Awareness into MLLMs via Reinforcement Learning for Deep Imbalanced Regression
Yao Du, Shanshan Song, Xiaomeng Li
Multimodal large language models (MLLMs) struggle with numerical regression under long-tailed target distributions. Token-level supervised fine-tuning (SFT) and point-wise regressi…
cs.LG2025
Contrastive Learning for Semi-Supervised Deep Regression with Generalized Ordinal Rankings from Spectral Seriation
Ce Wang, Weihang Dai, Hanru Bai +1
Contrastive learning methods enforce label distance relationships in feature space to improve representation capability for regression models. However, these methods highly depend…
cs.CV2024
Teach CLIP to Develop a Number Sense for Ordinal Regression
Yao Du, Qiang Zhai, Weihang Dai +1
Ordinal regression is a fundamental problem within the field of computer vision, with customised well-trained models on specific tasks. While pre-trained vision-language models (VL…