9 papers · 1 filter
Segmentation before Answering: Pixel Grounding for MLLM Visual Reasoning
Yake Wei, Yuan Wang, Fengyun Rao +2
Recent advancements in Multimodal Large Language Models (MLLMs) have evolved from static perception to interleaved visual-language reasoning, often referred to as ``thinking with i…
MIBench: Evaluating LMMs on Multimodal Interaction
Yu Miao, Zequn Yang, Yake Wei +5
In different multimodal scenarios, it needs to integrate and utilize information across modalities in a specific way based on the demands of the task. Different integration ways be…
MokA: Multimodal Low-Rank Adaptation for MLLMs
Yake Wei, Yu Miao, Dongzhan Zhou +1
In this paper, we reveal that most current efficient multimodal fine-tuning methods are hindered by a key limitation: they are directly borrowed from LLMs, often neglecting the int…
Patch Matters: Training-free Fine-grained Image Caption Enhancement via Local Perception
Ruotian Peng, Haiying He, Yake Wei +2
High-quality image captions play a crucial role in improving the performance of cross-modal applications such as text-to-image generation, text-to-video generation, and text-image…
On-the-fly Modulation for Balanced Multimodal Learning
Yake Wei, Di Hu, Henghui Du +1
Multimodal learning is expected to boost model performance by integrating information from different modalities. However, its potential is not fully exploited because the widely-us…
Diagnosing and Re-learning for Balanced Multimodal Learning
Yake Wei, Siwei Li, Ruoxuan Feng +1
To overcome the imbalanced multimodal learning problem, where models prefer the training of specific modalities, existing methods propose to control the training of uni-modal encod…