We introduce Flow-Based Distribution Matching (FBDM), which learns self-supervised representations through non-adversarial velocity regression. The encoder and auxiliary field are trained jointly by regressing analytic velocities along spherical paths from learned representations to reference targets. Both augmented views share a reference target, with a capacity limit on each reference center and an explicit view-alignment loss. A structured reference supports more components than the auxiliary flow dimension while retaining geometric separation. The auxiliary field is discarded after pretraining; only the backbone is retained downstream. On three ResNet-18 benchmarks with matched auxiliary parameter counts, FBDM provides 2.17 to 2.36 times higher per-epoch training throughput than critic-based Distribution Matching (DM), with about 0.21 GiB additional peak allocated GPU memory. Experiments show that FBDM is competitive with existing SSL methods from CIFAR to ImageNet and performs comparably to DM on their shared benchmarks. Under stated conditions, our population analysis bounds nearest-centroid classification error on the initial normalized pretraining representation by the FBDM loss.
Junbo Niu, Zheng Liu, Zhuangcheng Gu, Bin Wang, Linke Ouyang, Zhiyuan Zhao, Tao Chu, Tianyao He 等 59 位作者
We introduce MinerU2.5, a 1.2B-parameter document parsing vision-language model that achieves state-of-the-art recognition accuracy while maintaining exceptional computational efficiency. Our approach employs a coarse-to-fine, two-stage parsing strategy that decouples global layout analysis from local content recognition. In the first stage, the model performs efficient layout analysis on downsampled images to identify structural elements, circumventing the computational overhead of processing high-resolution inputs. In the second stage, guided by the global layout, it performs targeted content recognition on native-resolution crops extracted from the original image, preserving fine-grained details in dense text, complex formulas, and tables. To support this strategy, we developed a comprehensive data engine that generates diverse, large-scale training corpora for both pretraining and fine-tuning. Ultimately, MinerU2.5 demonstrates strong document parsing ability, achieving state-of-the-art performance on multiple benchmarks, surpassing both general-purpose and domain-specific models across various recognition tasks, while maintaining significantly lower computational overhead.
尚无 AI 解读，展示原文摘要
cs.CL · cs.CV
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表