Emergent Corpus Pre-training Benefits Vision Language Models
Abstract: Vision-Language Pre-trained Models (VL-PTMs) have achieved impressive performance across a wide range of tasks, but their success often hinges on access to large-scale multimodal datasets. While effective in high-resource settings, these models tend to struggle in data-scarce regimes. In this work, we investigate Emergent Communication (EC) as a mechanism to improve sample efficiency in VL-PTMs. We pre-train a Vision-Language Model (VLM) using EC tokens generated through a referential game between two artificial agents. Across three diverse cross-modal matching and reasoning benchmarks, EC pretraining yields substantial gains, improving Visual Referring Expression (VRE) accuracy by 108.6% and Visual Entailment (VE) by 69.6%. To further validate the the effectiveness of EC pretraining, we introduce LLaVA-1.5-EC, a LLaVA variant trained entirely on EC tokens. LLaVA-1.5-EC outperforms strong LVLM baselines, including BLIP-2 (13B), achieving relative gains of 104.23% on VizWiz, 34.8% on GQA, and 10.8% on VQAv2, and top performance on MMBench, a challenging instruction-following benchmark. These results highlight the transferability and generalization capacity of EC pretraining and underscore the potential of leveraging grounded EC tokens to enhance vision-language reasoning in low-resource settings, especially in settings with limited natural language data. We discuss implications and propose avenues for future research to explore the connections between EC and VL for multimodal understanding and effective human-machine communication. Code and data are available at anonymized link.
Submission Length: Long submission (more than 12 pages of main content)
Assigned Action Editor: ~Han-Jia_Ye1
Submission Number: 4621
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