Hard Negative Contrastive Learning for Fine-Grained Geometric Understanding in Large Multimodal Models
Journal:
arXiv
Published Date:
May 26, 2025
Abstract
Benefiting from contrastively trained visual encoders on large-scale natural
scene images, Large Multimodal Models (LMMs) have achieved remarkable
performance across various visual perception tasks. However, the inherent
limitations of contrastive learning upon summarized descriptions fundamentally
restrict the capabilities of models in meticulous reasoning, particularly in
crucial scenarios of geometric problem-solving. To enhance geometric
understanding, we propose a novel hard negative contrastive learning framework
for the vision encoder, which combines image-based contrastive learning using
generation-based hard negatives created by perturbing diagram generation code,
and text-based contrastive learning using rule-based negatives derived from
modified geometric descriptions and retrieval-based negatives selected based on
caption similarity. We train CLIP using our strong negative learning method,
namely MMCLIP (Multimodal Math CLIP), and subsequently train an LMM for
geometric problem-solving. Experiments show that our trained model, MMGeoLM,
significantly outperforms other open-source models on three geometric reasoning
benchmarks. Even with a size of 7B, it can rival powerful closed-source models
like GPT-4o. We further study the impact of different negative sample
construction methods and the number of negative samples on the geometric
reasoning performance of LMM, yielding fruitful conclusions. The code and
dataset are available at https://github.com/THU-KEG/MMGeoLM.
