From the 2 of 6 linked papers with an AI index.
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LaME: Learning to Think in Latent Space for Multimodal Embedding via Information Bottleneck
Peixi Wu, Biao Yang, Feipeng Ma +7
The paper introduces LaME, a multimodal embedding model that performs reasoning in a compact latent space using learnable tokens and an information‑bottleneck objective, eliminatin…
Beyond Chain-of-Thought: Rewrite as a Universal Interface for Generative Multimodal Embeddings
Peixi Wu, Ke Mei, Feipeng Ma +15
The paper introduces RIME, a rewrite-driven framework that improves multimodal embeddings by jointly optimizing generation and retrieval-friendly rewriting, aligning generative and…
WeThink: Toward General-purpose Vision-Language Reasoning via Reinforcement Learning
Jie Yang, Feipeng Ma, Zitian Wang +4
Building on the success of text-based reasoning models like DeepSeek-R1, extending these capabilities to multimodal reasoning holds great promise. While recent works have attempted…
EE-MLLM: A Data-Efficient and Compute-Efficient Multimodal Large Language Model
Feipeng Ma, Yizhou Zhou, Zheyu Zhang +7
Recent advancements in Multimodal Large Language Models (MLLMs) have demonstrated satisfactory performance across various vision-language tasks. Current approaches for vision and l…
Visual Perception by Large Language Model's Weights
Feipeng Ma, Hongwei Xue, Guangting Wang +7
Existing Multimodal Large Language Models (MLLMs) follow the paradigm that perceives visual information by aligning visual features with the input space of Large Language Models (L…
Multi-Modal Generative Embedding Model
Feipeng Ma, Hongwei Xue, Guangting Wang +7
Most multi-modal tasks can be formulated into problems of either generation or embedding. Existing models usually tackle these two types of problems by decoupling language modules…