- The paper's main contribution is the development of VisVM, which uses temporal difference learning to predict long-term visual-text alignment for coherent caption generation.
- It employs CLIP’s text-image similarity metric as a reward signal, reducing hallucinations from 32.4 to 26.2 and enhancing overall caption quality.
- VisVM-guided search is preferred 74% of the time over greedy methods and boosts self-training performance by 10.8% across eight multimodal benchmarks.
Scaling Inference-Time Search with Vision Value Model for Improved Visual Comprehension
This paper introduces the Vision Value Model (VisVM), which is designed to enhance the inference-time search in Vision-LLMs (VLMs) for generating image descriptions with improved accuracy and detail, reducing the occurrence of hallucinations. The VisVM presents a novel approach by evaluating not only the immediate quality of generated sentences but also anticipating the quality of subsequent sentences. This anticipatory capability allows VisVM to steer VLMs towards more coherent and detailed image descriptions.
Key Contributions and Methodology
The key contribution of this paper is the development of a stepwise value model—VisVM—that provides long-term vision value signals. This model significantly diverges from traditional process reward models by using Temporal Difference (TD) learning to predict long-term consequences, thereby maintaining coherence. VisVM employs CLIP's text-image similarity metric as a reward signal to evaluate the overall visual-text alignment and coherence.
To validate the effectiveness of VisVM, the authors conducted several experiments, indicating that the VisVM-guided search markedly enhances VLM's ability to generate descriptive captions compared to greedy decoding and other search methods leveraging visual reward signals. Specifically, captions produced using VisVM-guided search were preferred 74% of the time over those generated by greedy search, according to both GPT and human evaluations. Furthermore, VisVM-guided captions were used for self-training the VLM (LLaVA-Next-7B), showing an average performance improvement of 10.8% across eight multimodal benchmarks.
Experimental Results
The empirical results demonstrated a substantial reduction in hallucinations and an increase in the richness of visual details when VisVM was applied. The reduction in CHAIRs (a metric evaluating hallucination) from 32.4 to 26.2 illustrates the effectiveness of VisVM in producing more accurate image descriptions. Moreover, when scaling the inference compute, VisVM-guided search showed superior performance and computational efficiency, compared to CLIP-PRM-guided search.
Implications and Future Directions
The implications of this research are twofold. Practically, VisVM shows the potential to improve real-world applications of VLMs by enhancing the descriptiveness and accuracy of image captions and thus improving user interaction experiences with AI systems. Theoretically, it opens avenues for future research in self-improving models where VisVM-guided outputs can be progressively integrated into model training to further augment the model's capabilities without additional external data. This approach contributes to reducing the cost and time associated with extensive data labeling efforts.
The paper foresees future work in expanding the VisVM framework to other types of models and tasks, potentially paving the way for a robust self-training pipeline for VLMs, enabling ongoing performance improvements through inference-time search. Such developments would significantly advance the fields of AI and multimodal learning by offering scalable solutions to common challenges associated with large-scale vision-LLM training and deployment.
In conclusion, this paper presents a compelling methodology and substantiates the viability of inference-time interventions such as VisVM in scaling VLM performance, promising impactful enhancements in the generation of visually and contextually aware AI outputs.