Recent visual-text compression (VTC) methods, typified by DeepSeek-OCR, report impressive high token compression ratios for long-context modeling tasks by leveraging text-to-image rendering. However, existing evaluation protocols heavily rely on downstream task performance. Such evaluation metrics fail to accurately measure text preservation due to the strong inherent linguistic priors of Multimodal Large Language Models (MLLMs). In this work, we introduce a new evaluation framework that decouples MLLMs'capabilities to faithfully assess VTC quality. Within this framework, we further introduce the ZeroSense Benchmark to ensure low semantic correlation of testing samples. By eliminating textual dependencies, our benchmark guarantees that the evaluation results are purely reflective of VTC quality, unaffected by the semantic inference capabilities of downstream models. Extensive experiments across multiple datasets demonstrate that VTC quality and downstream task accuracy diverge significantly, highlighting the necessity of our decoupled evaluation framework.
Yonghan Gao, Zehong Chen, Lijian Xu et al.· 0 citations
Introduction The superior clinical utility of 7T magnetic resonance imaging (MRI) is constrained by high acquisition costs and limited scanner availability. While deep learning-based 3T-to-7T synthesis offers a potential solution, prevailing models typically rely on heavy parameterization, which increases computational redundancy and risk of overfitting on restricted medical datasets. In this paper, we focus on model efficiency and propose LiteMamba-Synth, an architecturally streamlined state space framework designed for high-fidelity MRI translation with minimal resource requirements. Methods Our core contribution is the integration of the ConvMamba block, which utilizes the linear-time complexity of State Space Models (SSMs) to capture expansive spatial dependencies without the prohibitive computational overhead of traditional attention mechanisms. To preserve essential anatomical details during the compression of the feature space, we introduce the Wavelet-Enhanced Skip connection (WES), a module that facilitates multi-scale frequency-domain feature fusion to safeguard high-frequency textures and edge information. Additionally, a lightweight Convolutional Block Attention Module (CBAM) is incorporated to adaptively recalibrate feature responses toward salient neuroanatomical regions. Results Experimental results on the UNC T1w dataset demonstrate that LiteMamba-Synth achieves a competitive PSNR of 20.82 dB and an SSIM of 0.711. Crucially, our model maintains a compact footprint of merely 2.15 million parameters, representing a substantial reduction in complexity compared to contemporary deep learning baselines. Discussion By delivering high-quality synthesis results with minimal parameter overhead, LiteMamba-Synth provides a practical and scalable solution for deploying advanced MRI synthesis in resource-constrained clinical environments.
Zhengrui Zhang, Jie Dong, Haoting Yang et al.· Frontiers in Neuroanatomy· 0 citations