Learning by Correction: Efficient Tuning Task for Zero-Shot Generative Vision-Language Reasoning

• URL: http://arxiv.org/abs/2404.00909v1
• Date: Mon, 1 Apr 2024 04:28:01 GMT
• Title: Learning by Correction: Efficient Tuning Task for Zero-Shot Generative Vision-Language Reasoning
• Authors: Rongjie Li, Yu Wu, Xuming He,
• Abstract summary: Generative vision-language models (VLMs) have shown impressive performance in zero-shot vision-language tasks like image captioning and visual question answering. We introduce Image-Conditioned Caption Correction (ICCC), a novel pre-training task designed to enhance ICCC's zero-shot performance without the need for labeled task. Experimental results on BLIP-2 and InstructBLIP demonstrate significant improvements in zero-shot image-text generation-based tasks through ICCC instruction tuning.
• Score: 22.93684323791136
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generative vision-language models (VLMs) have shown impressive performance in zero-shot vision-language tasks like image captioning and visual question answering. However, improving their zero-shot reasoning typically requires second-stage instruction tuning, which relies heavily on human-labeled or large language model-generated annotation, incurring high labeling costs. To tackle this challenge, we introduce Image-Conditioned Caption Correction (ICCC), a novel pre-training task designed to enhance VLMs' zero-shot performance without the need for labeled task-aware data. The ICCC task compels VLMs to rectify mismatches between visual and language concepts, thereby enhancing instruction following and text generation conditioned on visual inputs. Leveraging language structure and a lightweight dependency parser, we construct data samples of ICCC task from image-text datasets with low labeling and computation costs. Experimental results on BLIP-2 and InstructBLIP demonstrate significant improvements in zero-shot image-text generation-based VL tasks through ICCC instruction tuning.

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