Over-parameterization is critical for optimizing neural networks, whereas training sparse networks directly often fails to achieve satisfactory performance. However, the Lottery Ticket Hypothesis (LTH) demonstrates that a randomly initialized dense model has a sparse subnetwork that can be identified through iterative magnitude pruning, while maintaining strong trainability. In this study, we examine a complementary perspective on LTH and propose the Dual Lottery Ticket Hypothesis (DLTH), enabling general training for sparse networks. Specifically, DLTH asserts that any randomly chosen subnetwork drawn from its dense counterparts can be converted to a state with promising trainability. To validate DLTH, we introduce Sharpness-Aware Random Sparse Network Transformation (SA-RST), which uses a regularization term to extrude information from weights slated for masking. Further, this extrusion process is dynamically adjusted based on model sharpness during optimization. Experiments using both CNN and Transformer architectures across several benchmarks demonstrate the effectiveness of our SA-RST and validate the proposed DLTH. Furthermore, we provide comprehensive comparisons with dynamic sparse training methods (RigL, SET) and conduct ablation studies on the sharpness-aware component to validate our design choices. Our study is expected to yield new insights into general sparse network training.
Yue Bai, Mingyuan Zhang, Huan Wang et al.· IEEE Transactions on Pattern...· 0 citations
Layout understanding, or the interpretation of element organization, is essential for document analysis, user interface (UI) creation, and graphic design. While recent vision-language models (VLMs) excel at interpreting atomic layouts composed of independent elements, they struggle with compositional layouts that require reasoning over visually entangled elements within hierarchical multi-layer structures. In this paper, we introduce a new task, compositional layout understanding, and present CoDeLayout, a VQA dataset of ~20K real-world multi-layer layouts annotated with compositional element pairs and design intent. Through empirical analysis on CoDeLayout, we identify two key challenges for existing VLMs: semantic drift between textual metadata and visual content, and structural ambiguity in hierarchical inter-element relationships. To address these challenges, we propose MASON, a post-training paradigm that integrates multimodal alignment (MA) and structural perception (SP). MA enhances element interpretation by grounding metadata-defined elements to their visual counterparts, mitigating semantic drift, while SP models layer-aware inter-element spatial relationships to improve hierarchical understanding and reduce structural ambiguity. Experiments reveal substantial gaps in existing VLMs: even the strongest baseline, GPT-o3, achieves only 79.68% accuracy, whereas Qwen2.5-VL 7B with MASON reaches 91.66%. Notably, MASON surpasses full-data Direct Finetune using only 30% of the training data and scales better with additional data.
Yiyang Huang, Zhaowen Wang, Simon Jenni et al.· 0 citations
