2 citations · 4 across the 5 of their papers we have counts for
5 papers
Reciprocal Co-Training (RCT): Coupling Gradient-Based and Non-Differentiable Models via Reinforcement Learning
Yunshuo Tian, Akayou Kitessa, Tanuja Chitnis +1
Language models (LMs) and classical machine learning methods offer complementary strengths for predictive modeling, yet their fundamentally different representations and training p…
Open-Qwen2VL: Compute-Efficient Pre-Training of Fully-Open Multimodal LLMs on Academic Resources
Weizhi Wang, Yu Tian, Linjie Yang +2
The reproduction of state-of-the-art multimodal LLM pre-training faces barriers at every stage of the pipeline, including high-quality data filtering, multimodal data mixture strat…
VMAS: Video-to-Music Generation via Semantic Alignment in Web Music Videos
Yan-Bo Lin, Yu Tian, Linjie Yang +2
We present a framework for learning to generate background music from video inputs. Unlike existing works that rely on symbolic musical annotations, which are limited in quantity a…
Finetuned Multimodal Language Models Are High-Quality Image-Text Data Filters
Weizhi Wang, Khalil Mrini, Linjie Yang +4
We propose a novel framework for filtering image-text data by leveraging fine-tuned Multimodal Language Models (MLMs). Our approach outperforms predominant filtering methods (e.g.,…
The Devil is in the Details: A Deep Dive into the Rabbit Hole of Data Filtering
Haichao Yu, Yu Tian, Sateesh Kumar +2
The quality of pre-training data plays a critical role in the performance of foundation models. Popular foundation models often design their own recipe for data filtering, which ma…