Scaling Inference-Time Search with Vision Value Model for Improved
  Visual Comprehension
Authors: name: Wang Xiyao, name: Yang Zhengyuan, name: Li Linjie, name: Lu Hongjin, name: Xu Yuancheng, name: Lin Chung-Ching Lin, name: Lin Kevin, name: Huang Furong, name: Wang Lijuan

Despite significant advancements in vision-language models (VLMs), there
lacks effective approaches to enhance response quality by scaling
inference-time computation. This capability is known to be a core step towards
the self-improving models in recent large language model studies. In this
paper, we present Vision Value Model (VisVM) that can guide VLM inference-time
search to generate responses with better visual comprehension. Specifically,
VisVM not only evaluates the generated sentence quality in the current search
step, but also anticipates the quality of subsequent sentences that may result
from the current step, thus providing a long-term value. In this way, VisVM
steers VLMs away from generating sentences prone to hallucinations or
insufficient detail, thereby producing higher quality responses. Experimental
results demonstrate that VisVM-guided search significantly enhances VLMs'
ability to generate descriptive captions with richer visual details and fewer
hallucinations, compared with greedy decoding and search methods with other
visual reward signals. Furthermore, we find that self-training the model with
the VisVM-guided captions improve VLM's performance across a wide range of
multimodal benchmarks, indicating the potential for developing self-improving
VLMs. Our value model and code are available at
https://github.com/si0wang/VisVM.