BalCapRL: A Balanced Framework for RL-Based MLLM Image Captioning
Image captioning represents a cornerstone challenge in the realm of computer vision, especially with the rise of multimodal large language models (MLLMs). As the demand for more nuanced and accurate descriptions of images escalates, researchers are increasingly leveraging reinforcement learning (RL) techniques to enhance caption generation. However, the current landscape of RL-based captioning methods and their evaluative metrics often adhere to a limited perspective on caption quality, resulting in trade-offs across essential elements of captioning.
The prevalent utility-oriented approaches may lead to captions that, while enhancing downstream tasks like question answering, can also produce noisy, hallucinated, or excessively lengthy outputs that detract from overall fluency. Conversely, arena-style objectives tend to favor the generation of fluent yet generic descriptions, which might lack practical utility. To bridge this gap, the newly proposed framework, BalCapRL, aims to provide a more balanced approach to optimizing image captions by simultaneously addressing three core dimensions: utility-aware correctness, reference coverage, and linguistic quality.
Key Features of BalCapRL
The BalCapRL framework introduces several novel strategies designed to enhance the quality of generated captions. These include:
- Continuous Multi-Objective Reward Formulation: By utilizing a decoupled normalization approach inspired by GDPO (Gradient Decoupled Policy Optimization), BalCapRL effectively optimizes the rewards associated with continuous-valued captioning. This method promotes a more holistic assessment of caption quality compared to traditional GRPO methods.
- Length-Conditional Reward Masking: To address the issue of caption length, BalCapRL incorporates a penalty mechanism that is tailored to the specific length of the captions. This innovation ensures that the generated captions remain concise while retaining their informative value.
- Performance across Diverse Models: The framework has been tested on various MLLM architectures, including LLaVA-1.5-7B and Qwen2.5-VL models of 3B and 7B parameters. Results indicate consistent improvements in caption quality.
Performance Metrics
BalCapRL has demonstrated significant enhancements in captioning efficacy across multiple evaluation metrics. The framework achieved peak improvements of:
- +13.6 DCScore: This metric reflects the model’s ability to generate detailed and contextually accurate captions.
- +9.0 CaptionQA: An essential measure of how well the captions support downstream question-answering tasks.
- +29.0 CapArena: This metric evaluates the overall quality and richness of the generated captions in a competitive arena setting.
The introduction of BalCapRL marks a significant advancement in the field of image captioning, providing a balanced framework that addresses both the quality and utility of generated captions. As researchers continue to explore the capabilities of MLLMs, frameworks like BalCapRL are poised to reshape the landscape of image captioning, offering more accurate, fluent, and contextually relevant descriptions that cater to diverse applications.
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