Li Feng

YNIMG Journal 2026 Journal Article

Distinct and coordinated contributions of hippocampus and frontal eye field to novelty exploration and revisitation

• Ziwei Tian
• Sha Huang
• Dingyang Liu
• Zhiquan Yang
• Sushan Li
• Bingliang Hu
• Quan Wang
• Li Feng

AAAI Conference 2026 Conference Paper

Hierarchical Schedule Optimization for Fast and Robust Diffusion Model Sampling

• Aihua Zhu
• Rui Su
• Qinglin Zhao
• Li Feng
• Meng Shen
• Shibo He

Diffusion probabilistic models have set a new standard for generative fidelity but are hindered by a slow iterative sampling process. A powerful training-free strategy to accelerate this process is Schedule Optimization, which aims to find an optimal distribution of timesteps for a fixed and small Number of Function Evaluations (NFE) to maximize sample quality. To this end, a successful schedule optimization method must adhere to four core principles: effectiveness, adaptivity, practical robustness, and computational efficiency. However, existing paradigms struggle to satisfy these principles simultaneously, motivating the need for a more advanced solution. To overcome these limitations, we propose the Hierarchical-Schedule-Optimizer (HSO), a novel and efficient bi-level optimization framework. HSO reframes the search for a globally optimal schedule into a more tractable problem by iteratively alternating between two synergistic levels: an upper-level global search for an optimal initialization strategy and a lower-level local optimization for schedule refinement. This process is guided by two key innovations: the Midpoint Error Proxy (MEP), a solver-agnostic and numerically stable objective for effective local optimization, and the Spacing-Penalized Fitness (SPF) function, which ensures practical robustness by penalizing pathologically close timesteps. Extensive experiments show that HSO sets a new state-of-the-art for training-free sampling in the extremely low-NFE regime. For instance, with an NFE of just 5, HSO achieves a remarkable FID of 11.94 on LAION-Aesthetics with Stable Diffusion v2.1. Crucially, this level of performance is attained not through costly retraining, but with a one-time optimization cost of less than 8 seconds, presenting a highly practical and efficient paradigm for diffusion model acceleration.

JBHI Journal 2022 Journal Article

Particle-Based Calculation and Visualization of Protein Cavities Using SES Models

• Li Feng
• Feng Wang
• Jian Zhang
• Yong Tang
• Jing Zhao
• Lisha Zhou
• Jiayan Wang
• Dongliang Guo

The analysis of molecular cavities, where ligands interact with protein structures, plays a critical role in protein structure-based drug design. However, it is a challenge because of the ambiguous definition of the cavity boundaries in most cavity detection methods. The cavities are mostly calculated by input parameters, which are difficult for users to visualize cavities in interactive ways. In this paper, we propose a novel method for the interactive exploration of cavity calculation and visualization. Firstly, the proposed method combines the two solvent-excluded surfaces (SES) models of a given protein to define the boundaries and provides cavity emission points. Secondly, the system provides a user-guided interactive method to allow users to select cavities by simply clicking operations and to track the cavity identify and filling process based on position constraints. Finally, the selected cavities are represented with the colorful depth perception method. Experiments show that our work can effectively identify and calculate cavities.

YNIMG Journal 2020 Journal Article

Rapid golden-angle diffusion-weighted propeller MRI for simultaneous assessment of ADC and IVIM

• Qiuting Wen
• Li Feng
• Kun Zhou
• Yu-Chien Wu

JBHI Journal 2019 Journal Article

Deep Learning for Fall Detection: Three-Dimensional CNN Combined With LSTM on Video Kinematic Data

• Na Lu
• Yidan Wu
• Li Feng
• Jinbo Song

Fall detection is an important public healthcare problem. Timely detection could enable instant delivery of medical service to the injured. A popular nonintrusive solution for fall detection is based on videos obtained through ambient camera, and the corresponding methods usually require a large dataset to train a classifier and are inclined to be influenced by the image quality. However, it is hard to collect fall data and instead simulated falls are recorded to construct the training dataset, which is restricted to limited quantity. To address these problems, a three-dimensional convolutional neural network (3-D CNN) based method for fall detection is developed, which only uses video kinematic data to train an automatic feature extractor and could circumvent the requirement for large fall dataset of deep learning solution. 2-D CNN could only encode spatial information, and the employed 3-D convolution could extract motion feature from temporal sequence, which is important for fall detection. To further locate the region of interest in each frame, a long short-term memory (LSTM) based spatial visual attention scheme is incorporated. Sports dataset Sports-1 M with no fall examples is employed to train the 3-D CNN, which is then combined with LSTM to train a classifier with fall dataset. Experiments have verified the proposed scheme on fall detection benchmark with high accuracy as 100%. Superior performance has also been obtained on other activity databases.