5 papers
Exposure Bias Can Alleviate Itself via Directional and Frequency Rectification in Flow Matching
Guanbo Huang, Jingjia Mao, Fanding Huang +9
Flow Matching (FM) has achieved remarkable generative performance, yet it suffers from exposure bias due to discrepancies between training and inference. Existing mitigation strate…
Domain-Specific Data Synthesis for LLMs via Minimal Sufficient Representation Learning
Tong Ye, Hang Yu, Tengfei Ma +6
Large Language Models have demonstrated remarkable progress in general-purpose capabilities and can achieve strong performance in specific domains through fine-tuning on domain-spe…
DMC-CF: Dynamic Multimodal CounterFactual QA benchmark for Causal Reasoning
Junzhe Zhang, Huixuan Zhang, Guirong Wang +5
With the rapid advancement of multimodal large language models (MLLMs), models have demonstrated increasingly powerful multimodal capabilities. However, whether MLLMs trained throu…
Learning Dual Transformers for All-In-One Image Restoration from a Frequency Perspective
Jie Chu, Tong Su, Pei Liu +4
This work aims to tackle the all-in-one image restoration task, which seeks to handle multiple types of degradation with a single model. The primary challenge is to extract degrada…
Densely Distilling Cumulative Knowledge for Continual Learning
Zenglin Shi, Pei Liu, Tong Su +4
Continual learning, involving sequential training on diverse tasks, often faces catastrophic forgetting. While knowledge distillation-based approaches exhibit notable success in pr…