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cs.CV2025
MergeMix: A Unified Augmentation Paradigm for Visual and Multi-Modal Understanding
Xin Jin, Siyuan Li, Siyong Jian +2
Vision-language alignment in multi-modal large language models (MLLMs) relies on supervised fine-tuning (SFT) or reinforcement learning (RL). To align multi-modal large language mo…
cs.CV2025
MS-Mix: Sentiment-Guided Adaptive Augmentation for Multimodal Sentiment Analysis
Hongyu Zhu, Lin Chen, Xin Jin +1
Multimodal Sentiment Analysis (MSA) integrates complementary features from text, video, and audio for robust emotion understanding in human interactions. However, models suffer fro…
cs.LG2025
Taming LLMs by Scaling Learning Rates with Gradient Grouping
Siyuan Li, Juanxi Tian, Zedong Wang +4
Training large language models (LLMs) poses challenges due to their massive scale and heterogeneous architectures. While adaptive optimizers like AdamW help address gradient variat…