Language Grounded QFormer for Efficient Vision Language Understanding

• URL: http://arxiv.org/abs/2311.07449v1
• Date: Mon, 13 Nov 2023 16:30:49 GMT
• Title: Language Grounded QFormer for Efficient Vision Language Understanding
• Authors: Moulik Choraria, Nitesh Sekhar, Yue Wu, Xu Zhang, Prateek Singhal, Lav R. Varshney
• Abstract summary: We take inspiration from the Query Transformer (QFormer) approach proposed in BLIP-2 models for bridging frozen modalities. We propose a more efficient method for QFormer-based vision-language alignment.
• Score: 25.432918254523344
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large-scale pretraining and instruction tuning have been successful for training general-purpose language models with broad competencies. However, extending to general-purpose vision-language models is challenging due to the distributional diversity in visual inputs. A recent line of work explores vision-language instruction tuning, taking inspiration from the Query Transformer (QFormer) approach proposed in BLIP-2 models for bridging frozen modalities. However, these approaches rely heavily on large-scale multi-modal pretraining for representation learning before eventual finetuning, incurring a huge computational overhead, poor scaling, and limited accessibility. To that end, we propose a more efficient method for QFormer-based vision-language alignment and demonstrate the effectiveness of our strategy compared to existing baselines in improving the efficiency of vision-language pretraining.

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