1 citations · 1 across the 14 of their papers we have counts for
16 papers
SewFusion: Tailored Generation of Topology and Panel-Level Geometry for Sewing Patterns
Jiaxin Lin, Xiao Pan, Hangjie Yuan +3
Generating sewing patterns from images and text requires modeling a heterogeneous representation composed of discrete topology and continuous geometry. Existing methods mainly foll…
GUI-AC: Enhancing Continual Learning in GUI Agents
Can Lin, Wenjun Yang, Tao Feng +6
Graphical User Interfaces (GUIs) serve as the dominant medium for human-computer interaction, yet building GUI agents that generalize across the vast diversity of real-world interf…
Lumos-Nexus: Efficient Frequency Bridging with Homogeneous Latent Space for Video Unified Models
Jiazheng Xing, Hangjie Yuan, Lingling Cai +9
Connector-based video unified models have demonstrated strong capability in instruction-grounded video synthesis, but integrating a large high-fidelity generator into the unified t…
5% > 100%: Flatness Preference is All You Need for Multimodal Parameter-Efficient Fine-Tuning
Yifan Zhu, Can Lin, Hangjie Yuan +4
Parameter-Efficient Fine-Tuning (PEFT) methods provide a streamlined and efficient tool for adapting large models to domain-specific multimodal downstream tasks. Although these met…
Filter, Then Reweight: Rethinking Optimization Granularity in On-Policy Distillation
Yuying Li, Leqi Zheng, Yongzi Yu +6
On-Policy distillation (OPD) in large language models is shifting from full-trace KL supervision toward more selective training paradigms. Recent OPD methods increasingly focus on…
A Faster Path to Continual Learning
Wei Li, Hangjie Yuan, Zixiang Zhao +3
Continual Learning (CL) aims to train neural networks on a dynamic stream of tasks without forgetting previously learned knowledge. Among optimization-based approaches, C-Flat has…
CogFlow: Bridging Perception and Reasoning through Knowledge Internalization for Visual Mathematical Problem Solving
Shuhang Chen, Yunqiu Xu, Junjie Xie +7
Despite significant progress, multimodal large language models continue to struggle with visual mathematical problem solving. Some recent works recognize that visual perception is…
Why Does RL Generalize Better Than SFT? A Data-Centric Perspective on VLM Post-Training
Aojun Lu, Tao Feng, Hangjie Yuan +2
The adaptation of large-scale Vision-Language Models (VLMs) through post-training reveals a pronounced generalization gap: models fine-tuned with Reinforcement Learning (RL) consis…
Continual GUI Agents
Ziwei Liu, Borui Kang, Hangjie Yuan +4
As digital environments (data distribution) are in flux, with new GUI data arriving over time-introducing new domains or resolutions-agents trained on static environments deteriora…
SAMora: Enhancing SAM through Hierarchical Self-Supervised Pre-Training for Medical Images
Shuhang Chen, Hangjie Yuan, Pengwei Liu +3
The Segment Anything Model (SAM) has demonstrated significant potential in medical image segmentation. Yet, its performance is limited when only a small amount of labeled data is a…
C-Flat++: Towards a More Efficient and Powerful Framework for Continual Learning
Wei Li, Hangjie Yuan, Zixiang Zhao +4
Balancing sensitivity to new tasks and stability for retaining past knowledge is crucial in continual learning (CL). Recently, sharpness-aware minimization has proven effective in…
Rethinking the Stability-Plasticity Trade-off in Continual Learning from an Architectural Perspective
Aojun Lu, Hangjie Yuan, Tao Feng +1
The quest for Continual Learning (CL) seeks to empower neural networks with the ability to learn and adapt incrementally. Central to this pursuit is addressing the stability-plasti…
Branch, or Layer? Zeroth-Order Optimization for Continual Learning of Vision-Language Models
Ziwei Liu, Borui Kang, Wei Li +6
Vision-Language Continual Learning (VLCL) has attracted significant research attention for its robust capabilities, and the adoption of Parameter-Efficient Fine-Tuning (PEFT) strat…
Adapt before Continual Learning
Aojun Lu, Tao Feng, Hangjie Yuan +2
Continual Learning (CL) seeks to enable neural networks to incrementally acquire new knowledge (plasticity) while retaining existing knowledge (stability). Although pre-trained mod…
Generative Artificial Intelligence in Robotic Manipulation: A Survey
Kun Zhang, Peng Yun, Jun Cen +11
This survey provides a comprehensive review on recent advancements of generative learning models in robotic manipulation, addressing key challenges in the field. Robotic manipulati…
MathFlow: Enhancing the Perceptual Flow of MLLMs for Visual Mathematical Problems
Shuhang Chen, Hangjie Yuan, Yunqiu Xu +5
Despite strong results on many tasks, multimodal large language models (MLLMs) still underperform on visual mathematical problem solving, especially in reliably perceiving and inte…