arXiv:2410.07336v1 Announce Type: cross
Abstract: Despite significant advancements in caption generation, existing evaluation metrics often fail to capture the full quality or fine-grained details of captions. This is mainly due to their reliance on non-specific human-written references or noisy pre-training data. Still, finding an effective metric is crucial not only for captions evaluation but also for the generation phase. Metrics can indeed play a key role in the fine-tuning stage of captioning models, ultimately enhancing the quality of the generated captions. In this paper, we propose PAC-S++, a learnable metric that leverages the CLIP model, pre-trained on both web-collected and cleaned data and regularized through additional pairs of generated visual and textual positive samples. Exploiting this stronger and curated pre-training, we also apply PAC-S++ as a reward in the Self-Critical Sequence Training (SCST) stage typically employed to fine-tune captioning models. Extensive experiments on different image and video datasets highlight the effectiveness of PAC-S++ compared to popular metrics for the task, including its sensitivity to object hallucinations. Furthermore, we show that integrating PAC-S++ into the fine-tuning stage of a captioning model results in semantically richer captions with fewer repetitions and grammatical errors. Evaluations on out-of-domain benchmarks further demonstrate the efficacy of our fine-tuning approach in enhancing model capabilities. Source code and trained models are publicly available at: https://github.com/aimagelab/pacscore.
Enhancing Caption Generation with PAC-S++
Caption generation is a complex task that requires both understanding the visual content of an image or video and generating a coherent and informative description. While there have been significant advancements in caption generation, the evaluation metrics used to assess the quality of captions often fall short in capturing the nuances and fine-grained details. This is primarily because existing metrics rely on non-specific human-written references or noisy pre-training data.
In order to address this limitation, a group of researchers propose PAC-S++, a learnable metric that leverages the CLIP model. CLIP is a state-of-the-art model that has been pre-trained on a large dataset of images and text, allowing it to understand the relationship between visual and textual information. By leveraging CLIP’s strong pre-training, PAC-S++ is able to capture the quality and details of captions more effectively.
One of the key advantages of PAC-S++ is its use as a reward in the Self-Critical Sequence Training (SCST) stage, which is commonly employed to fine-tune captioning models. SCST is a method that uses reinforcement learning to train models to generate better captions by using the captions themselves as the reward signal. By incorporating PAC-S++ as a reward, the captioning models can be fine-tuned to generate semantically richer captions with fewer repetitions and grammatical errors.
The researchers conducted extensive experiments on various image and video datasets to evaluate the effectiveness of PAC-S++ compared to popular metrics used for caption evaluation. The results showed that PAC-S++ outperformed existing metrics in capturing fine-grained details and detecting object hallucinations in captions. It also demonstrated its sensitivity to these issues, which is crucial for generating high-quality captions.
Moreover, the researchers evaluated their fine-tuning approach using out-of-domain benchmarks, demonstrating its efficacy in enhancing the capabilities of captioning models even in unfamiliar domains. This highlights the potential of PAC-S++ to be applied to a wide range of multimedia information systems, including animations, artificial reality, augmented reality, and virtual realities.
To further support the research community, the source code and trained models of PAC-S++ are publicly available on GitHub, allowing other researchers to reproduce the results and build upon this work.
Conclusion
PAC-S++ is a learnable metric that leverages the CLIP model to enhance caption generation. By incorporating PAC-S++ as a reward in the fine-tuning stage of captioning models, semantically richer captions with fewer repetitions and grammatical errors can be generated. The effectiveness of PAC-S++ was demonstrated through extensive experiments on various datasets and its sensitivity to object hallucinations. The proposed approach is not only relevant to caption generation but also has implications for multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. The availability of the source code and trained models further encourages collaboration and advances in the field.
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