Qifan Zhang
Publications
GVR-Coder: A Visual-Feedback Framework for Structured SVG Generation in Complex Document and Meeting Scenarios
In demanding professional environments and meeting review scenarios, lengthy text often imposes a high cognitive load. To facilitate efficient information communication, transforming verbose text into logically clear diagrams is essential. Scalable Vector Graphics (SVG) provide an effective representation for this purpose due to their editability and resolution independence. However, current research on Text-to-SVG generation remains hindered by three major challenges: (1) the scarcity of datasets for complex, logic-rich diagrams; (2) the absence of explicit layout priors, which leads to chaotic spatial arrangements; and (3) the lack of fine-grained visual feedback to validate rendered outputs and correct aesthetic defects. To address these challenges, at the data level, we introduce DocMeetSVG-100K, a large-scale SVG dataset tailored for document authoring and meeting review scenarios. At the model level, we propose GVR-Coder, a novel framework designed to generate high-quality logical diagrams from lengthy professional texts. Specifically, we adopt a curriculum-driven rejection sampling fine-tuning to progressively enhance the model's capability in modeling complex structures, while explicitly incorporating layout constraint knowledge during training. In addition, we introduce reinforcement learning from dual rendering feedback, a mechanism that provides implicit feedback through reward signals to jointly optimize structural complexity and visual aesthetics. Furthermore, we design a generate-verify-repair agent loop, which improves generation quality through explicit, fine-grained feedback and targeted refinement. Extensive experiments demonstrate that GVR-Coder outperforms competitive baselines and reliably produces logically coherent and visually appealing diagrams. Code and data are available at https://github.com/CurryaNa/GVR-Coder.
Continual Distillation Learning for Rehearsal-Free Class-Incremental Learning via Decoupled Prompting
Prompt-based continual learning has shown strong performance in rehearsal-free class-incremental learning by adapting learnable prompts while freezing a pre-trained Vision Transformer (ViT) backbone. However, the effect of backbone scale remains underexplored. We observe that larger ViT backbones consistently yield better continual learning performance, which motivates us to study how to transfer such capability from a larger model to a smaller one. In this paper, we introduce Continual Distillation Learning (CDL), a new setting for knowledge distillation in rehearsal-free prompt-based continual learning. We show that conventional distillation methods provide only limited gains in CDL, mainly because task-specific prompts are forced to encode both continual adaptation and distillation knowledge, while lacking a persistent mechanism for cross-task knowledge transfer. To address this problem, we propose Decoupled Continual Distillation Learning (D-CDL), which introduces persistent Knowledge-Distillation prompts (KD-prompts) and a dedicated KD branch to explicitly decouple distillation from task adaptation. The proposed KD-prompts are propagated across tasks as a global carrier of teacher knowledge, while the original prompts remain responsible for continual learning. D-CDL is simple, general, and can be integrated into various prompt-based continual learning frameworks. Extensive experiments on Split CIFAR-100 and Split ImageNet-R across four representative continual learning methods show that D-CDL consistently outperforms existing distillation baselines and substantially improves student performance under different teacher-student settings.