1 citations · 2 across the 14 of their papers we have counts for
15 papers
Bridging Body and Brain: Gene-Driven Morphology--Control Co-Design
Fu Feng, Ruixiao Shi, Yucheng Xie +2
Morphology--control co-design jointly optimizes an agent's body structure and control policy as an integrated embodied system. However, existing methods typically model morphology…
SafeGene: Reusable Adapters for Transferable Safety Alignment
Yanghan Wang, Zhiqiang Kou, Fu Feng +2
Open-weight LLMs are increasingly fine-tuned into customized assistants, but downstream fine-tuning can weaken safety alignment and make models more vulnerable to malicious prompts…
Constraint-based Pre-training: From Structured Constraints to Scalable Model Initialization
Fu Feng, Yucheng Xie, Ruixiao Shi +2
The pre-training and fine-tuning paradigm has become the dominant approach for model adaptation. However, conventional pre-training typically yields models at a fixed scale, wherea…
A Creative Agent is Worth a 64-Token Template
Ruixiao Shi, Fu Feng, Yucheng Xie +3
Text-to-image (T2I) models have substantially improved image fidelity and prompt adherence, yet their creativity remains constrained by reliance on discrete natural language prompt…
Self-Supervised Weight Templates for Scalable Vision Model Initialization
Yucheng Xie, Fu Feng, Ruixiao Shi +3
The increasing scale and complexity of modern model parameters underscore the importance of pre-trained models. However, deployment often demands architectures of varying sizes, ex…
Knowledge Diversion for Efficient Morphology Control and Policy Transfer
Fu Feng, Ruixiao Shi, Yucheng Xie +3
Universal morphology control aims to learn a universal policy that generalizes across heterogeneous agent morphologies, with Transformer-based controllers emerging as a popular cho…
FAD: Frequency Adaptation and Diversion for Cross-domain Few-shot Learning
Ruixiao Shi, Fu Feng, Yucheng Xie +2
Cross-domain few-shot learning (CD-FSL) requires models to generalize from limited labeled samples under significant distribution shifts. While recent methods enhance adaptability…
Distribution-Conditional Generation: From Class Distribution to Creative Generation
Fu Feng, Yucheng Xie, Xu Yang +2
Text-to-image (T2I) diffusion models are effective at producing semantically aligned images, but their reliance on training data distributions limits their ability to synthesize tr…
Towards Better Performance in Incomplete LDL: Addressing Data Imbalance
Zhiqiang Kou, Haoyuan Xuan, Jing Wang +2
Label Distribution Learning (LDL) is a novel machine learning paradigm that addresses the problem of label ambiguity and has found widespread applications. Obtaining complete label…
Redefining <Creative> in Dictionary: Towards an Enhanced Semantic Understanding of Creative Generation
Fu Feng, Yucheng Xie, Xu Yang +2
``Creative'' remains an inherently abstract concept for both humans and diffusion models. While text-to-image (T2I) diffusion models can easily generate out-of-distribution concept…
FINE: Factorizing Knowledge for Initialization of Variable-sized Diffusion Models
Yucheng Xie, Fu Feng, Ruixiao Shi +4
The training of diffusion models is computationally intensive, making effective pre-training essential. However, real-world deployments often demand models of variable sizes due to…
WAVE: Weight Templates for Adaptive Initialization of Variable-sized Models
Fu Feng, Yucheng Xie, Jing Wang +1
The growing complexity of model parameters underscores the significance of pre-trained models. However, deployment constraints often necessitate models of varying sizes, exposing l…
Inaccurate Label Distribution Learning with Dependency Noise
Zhiqiang Kou, Jing Wang, Yuheng Jia +1
In this paper, we introduce the Dependent Noise-based Inaccurate Label Distribution Learning (DN-ILDL) framework to tackle the challenges posed by noise in label distribution learn…
Transferring Core Knowledge via Learngenes
Fu Feng, Jing Wang, Xin Geng
The pre-training paradigm fine-tunes the models trained on large-scale datasets to downstream tasks with enhanced performance. It transfers all knowledge to downstream tasks withou…
RankMatch: A Novel Approach to Semi-Supervised Label Distribution Learning Leveraging Inter-label Correlations
Kouzhiqiang Yucheng Xie, Jing Wang, Yuheng Jia +2
This paper introduces RankMatch, an innovative approach for Semi-Supervised Label Distribution Learning (SSLDL). Addressing the challenge of limited labeled data, RankMatch effecti…