32 citations · 67 across the 16 of their papers we have counts for
18 papers
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…
Information-Theoretic Decomposition for Multimodal Interaction Learning
Zequn Yang, Yake Wei, Haotian Ni +2
Multimodal learning hinges on capturing redundant, unique, and synergistic information across modalities, which collectively constitute multimodal interactions. A critical yet unde…
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…
RollingQ: Reviving the Cooperation Dynamics in Multimodal Transformer
Haotian Ni, Yake Wei, Hang Liu +4
Multimodal learning faces challenges in effectively fusing information from diverse modalities, especially when modality quality varies across samples. Dynamic fusion strategies, s…
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…
Adaptive Unimodal Regulation for Balanced Multimodal Information Acquisition
Chengxiang Huang, Yake Wei, Zequn Yang +1
Sensory training during the early ages is vital for human development. Inspired by this cognitive phenomenon, we observe that the early training stage is also important for the mul…
Enhancing Modality Representation and Alignment for Multimodal Cold-start Active Learning
Meng Shen, Yake Wei, Jianxiong Yin +3
Training multimodal models requires a large amount of labeled data. Active learning (AL) aim to reduce labeling costs. Most AL methods employ warm-start approaches, which rely on s…
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…
MMPareto: Boosting Multimodal Learning with Innocent Unimodal Assistance
Yake Wei, Di Hu
Multimodal learning methods with targeted unimodal learning objectives have exhibited their superior efficacy in alleviating the imbalanced multimodal learning problem. However, in…
Multimodal Fusion on Low-quality Data: A Comprehensive Survey
Qingyang Zhang, Yake Wei, Zongbo Han +8
Multimodal fusion focuses on integrating information from multiple modalities with the goal of more accurate prediction, which has achieved remarkable progress in a wide range of s…
Quantifying and Enhancing Multi-modal Robustness with Modality Preference
Zequn Yang, Yake Wei, Ce Liang +1
Multi-modal models have shown a promising capability to effectively integrate information from various sources, yet meanwhile, they are found vulnerable to pervasive perturbations,…
Enhancing multimodal cooperation via sample-level modality valuation
Yake Wei, Ruoxuan Feng, Zihe Wang +1
One primary topic of multimodal learning is to jointly incorporate heterogeneous information from different modalities. However most models often suffer from unsatisfactory multimo…
Learning in Audio-visual Context: A Review, Analysis, and New Perspective
Yake Wei, Di Hu, Yapeng Tian +1
Sight and hearing are two senses that play a vital role in human communication and scene understanding. To mimic human perception ability, audio-visual learning, aimed at developin…
Learning to Answer Questions in Dynamic Audio-Visual Scenarios
Guangyao Li, Yake Wei, Yapeng Tian +3
In this paper, we focus on the Audio-Visual Question Answering (AVQA) task, which aims to answer questions regarding different visual objects, sounds, and their associations in vid…
Balanced Multimodal Learning via On-the-fly Gradient Modulation
Xiaokang Peng, Yake Wei, Andong Deng +2
Multimodal learning helps to comprehensively understand the world, by integrating different senses. Accordingly, multiple input modalities are expected to boost model performance,…
Class-aware Sounding Objects Localization via Audiovisual Correspondence
Di Hu, Yake Wei, Rui Qian +3
Audiovisual scenes are pervasive in our daily life. It is commonplace for humans to discriminatively localize different sounding objects but quite challenging for machines to achie…