Reproduce “MMEmb-R1: Reasoning-Enhanced Multimodal Embedding with Pair-Aware Selection and Adaptive Control” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Yuchi Wang, Dingkang Yang, Haiyang Yu, Weikang Bian, Jiefeng Long, Xiao Liang, Chao Feng, Hongsheng Li
MMEmb-R1 introduces a framework for multimodal embedding that integrates MLLM generative reasoning by modeling reasoning as a latent variable and adaptively controlling its invocation. The framework achieves state-of-the-art performance on the MMEB-V2 benchmark, with its 4B parameter model scoring 71.2, and improves inference throughput by up to 3.1x by selectively applying reasoning.
Key insights
- Modeling reasoning as a latent variable with diverse, pair-aware selection is crucial for integrating generative rationales effectively with pairwise contrastive embedding learning, preventing superficial matching.
- Adaptive invocation of reasoning is essential for optimizing performance and efficiency, as universally enforced reasoning can lead to "overthinking" on simple inputs and negatively impact results.
- Utilizing a diverse set of MLLM workers to generate reasoning candidates provides a more robust prior for identifying high-quality rationales compared to relying on a single teacher model.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.