2 citations · 2 across the 14 of their papers we have counts for
8 papers · 1 filter
Geometric Prior-Guided Federated Prompt Calibration
Fei Luo, Ziwei Zhao, Mingxuan Wang +5
Federated Prompt Learning (FPL) offers a parameter-efficient solution for collaboratively training large models, but its performance is severely hindered by data heterogeneity, whi…
From Perception to Cognition: A Survey of Vision-Language Interactive Reasoning in Multimodal Large Language Models
Chenyue Zhou, Mingxuan Wang, Yanbiao Ma +19
Multimodal Large Language Models (MLLMs) strive to achieve a profound, human-like understanding of and interaction with the physical world, but often exhibit a shallow and incohere…
Calibrating Biased Distribution in VFM-derived Latent Space via Cross-Domain Geometric Consistency
Yanbiao Ma, Wei Dai, Bowei Liu +5
Despite the fast progress of deep learning, one standing challenge is the gap of the observed training samples and the underlying true distribution. There are multiple reasons for…
Compositional Attribute Imbalance in Vision Datasets
Jiayi Chen, Yanbiao Ma, Andi Zhang +3
Visual attribute imbalance is a common yet underexplored issue in image classification, significantly impacting model performance and generalization. In this work, we first define…
Keeping Yourself is Important in Downstream Tuning Multimodal Large Language Model
Wenke Huang, Jian Liang, Xianda Guo +14
Multi-modal Large Language Models (MLLMs) integrate visual and linguistic reasoning to address complex tasks such as image captioning and visual question answering. While MLLMs dem…
Geometric Knowledge-Guided Localized Global Distribution Alignment for Federated Learning
Yanbiao Ma, Wei Dai, Wenke Huang +1
Data heterogeneity in federated learning, characterized by a significant misalignment between local and global distributions, leads to divergent local optimization directions and h…
Pursuing Better Decision Boundaries for Long-Tailed Object Detection via Category Information Amount
Yanbiao Ma, Wei Dai, Jiayi Chen
In object detection, the instance count is typically used to define whether a dataset exhibits a long-tail distribution, implicitly assuming that models will underperform on catego…
Geometric Origins of Bias in Deep Neural Networks: A Human Visual System Perspective
Yanbiao Ma, Bowei Liu, Andi Zhang
Bias formation in deep neural networks (DNNs) remains a critical yet poorly understood challenge, influencing both fairness and reliability in artificial intelligence systems. Insp…