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Jianfeng Feng

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31 papers

2 author rows

NeurIPS Conference 2025 Conference Paper

Stochastic Forward-Forward Learning through Representational Dimensionality Compression

Zhichao Zhu
Yang Qi
Hengyuan Ma
Wenlian Lu
Jianfeng Feng

The Forward-Forward (FF) learning algorithm provides a bottom-up alternative to backpropagation (BP) for training neural networks, relying on a layer-wise "goodness" function with well-designed negative samples for contrastive learning. Existing goodness functions are typically defined as the sum of squared postsynaptic activations, neglecting correlated variability between neurons. In this work, we propose a novel goodness function termed dimensionality compression that uses the effective dimensionality (ED) of fluctuating neural responses to incorporate second-order statistical structure. Our objective minimizes ED for noisy copies of individual inputs while maximizing it across the sample distribution, promoting structured representations without the need to prepare negative samples. We demonstrate that this formulation achieves competitive performance compared to other non-BP methods. Moreover, we show that noise plays a constructive role that can enhance generalization and improve inference when predictions are derived from the mean of squared output, which is equivalent to making predictions based on an energy term. Our findings contribute to the development of more biologically plausible learning algorithms and suggest a natural fit for neuromorphic computing, where stochasticity is a computational resource rather than a nuisance. The code is available at https: //github. com/ZhichaoZhu/StochasticForwardForward.

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NeurIPS Conference 2024 Conference Paper

DG-SLAM: Robust Dynamic Gaussian Splatting SLAM with Hybrid Pose Optimization

Yueming Xu
Haochen Jiang
Zhongyang Xiao
Jianfeng Feng
Li Zhang

Achieving robust and precise pose estimation in dynamic scenes is a significant research challenge in Visual Simultaneous Localization and Mapping (SLAM). Recent advancements integrating Gaussian Splatting into SLAM systems have proven effective in creating high-quality renderings using explicit 3D Gaussian models, significantly improving environmental reconstruction fidelity. However, these approaches depend on a static environment assumption and face challenges in dynamic environments due to inconsistent observations of geometry and photometry. To address this problem, we propose DG-SLAM, the first robust dynamic visual SLAM system grounded in 3D Gaussians, which provides precise camera pose estimation alongside high-fidelity reconstructions. Specifically, we propose effective strategies, including motion mask generation, adaptive Gaussian point management, and a hybrid camera tracking algorithm to improve the accuracy and robustness of pose estimation. Extensive experiments demonstrate that DG-SLAM delivers state-of-the-art performance in camera pose estimation, map reconstruction, and novel-view synthesis in dynamic scenes, outperforming existing methods meanwhile preserving real-time rendering ability.

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NeurIPS Conference 2024 Conference Paper

Efficient Combinatorial Optimization via Heat Diffusion

Hengyuan Ma
Wenlian Lu
Jianfeng Feng

Combinatorial optimization problems are widespread but inherently challenging due to their discrete nature. The primary limitation of existing methods is that they can only access a small fraction of the solution space at each iteration, resulting in limited efficiency for searching the global optimal. To overcome this challenge, diverging from conventional efforts of expanding the solver's search scope, we focus on enabling information to actively propagate to the solver through heat diffusion. By transforming the target function while preserving its optima, heat diffusion facilitates information flow from distant regions to the solver, providing more efficient navigation. Utilizing heat diffusion, we propose a framework for solving general combinatorial optimization problems. The proposed methodology demonstrates superior performance across a range of the most challenging and widely encountered combinatorial optimizations. Echoing recent advancements in harnessing thermodynamics for generative artificial intelligence, our study further reveals its significant potential in advancing combinatorial optimization.

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TMLR Journal 2024 Journal Article

LEA: Learning Latent Embedding Alignment Model for fMRI Decoding and Encoding

Xuelin Qian
Yikai Wang
Xinwei Sun
Yanwei Fu
Xiangyang Xue
Jianfeng Feng

The connection between brain activity and visual stimuli is crucial to understanding the human brain. Although deep generative models have shown advances in recovering brain recordings by generating images conditioned on fMRI signals, it is still challenging to generate consistent semantics. Moreover, predicting fMRI signals from visual stimuli remains a hard problem. In this paper, we introduce a unified framework that addresses both fMRI decoding and encoding. We train two latent spaces to represent and reconstruct fMRI signals and visual images, respectively. By aligning these two latent spaces, we seamlessly transform between the fMRI signal and visual stimuli. Our model, called Latent Embedding Alignment (LEA), can recover visual stimuli from fMRI signals and predict brain activity from images. LEA outperforms existing methods on multiple fMRI decoding and encoding benchmarks. It offers a comprehensive solution for modeling the relationship between fMRI signals and visual stimuli. The codes are available at \url{https://github.com/naiq/LEA}.

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IROS Conference 2024 Conference Paper

OpenOcc: Open Vocabulary 3D Scene Reconstruction via Occupancy Representation

Haochen Jiang
Yueming Xu
Yihan Zeng
Hang Xu 0004
Wei Zhang 0196
Jianfeng Feng
Li Zhang 0040

3D reconstruction has been widely used in autonomous navigation fields of mobile robotics. However, the former research can only provide the basic geometry structure without the capability of open-world scene understanding, limiting advanced tasks like human interaction and visual navigation. Moreover, traditional 3D scene understanding approaches rely on expensive labeled 3D datasets to train a model for a single task with supervision. Thus, geometric reconstruction with zero-shot scene understanding i. e. Open vocabulary 3D Understanding and Reconstruction, is crucial for the future development of mobile robots. In this paper, we propose OpenOcc, a novel framework unifying the 3D scene reconstruction and open vocabulary understanding with neural radiance fields. We model the geometric structure of the scene with occupancy representation and distill the pre-trained open vocabulary model into a 3D language field via volume rendering for zero-shot inference. Furthermore, a novel semantic-aware confidence propagation (SCP) method has been proposed to relieve the issue of language field representation degeneracy caused by inconsistent measurements in distilled features. Experimental results show that our approach achieves competitive performance in 3D scene understanding tasks, especially for small and long-tail objects.

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YNICL Journal 2023 Journal Article

Age-level bias correction in brain age prediction

Biao Zhang
Shuqin Zhang
Jianfeng Feng
Shihua Zhang

The predicted age difference (PAD) between an individual's predicted brain age and chronological age has been commonly viewed as a meaningful phenotype relating to aging and brain diseases. However, the systematic bias appears in the PAD achieved using machine learning methods. Recent studies have designed diverse bias correction methods to eliminate it for further downstream studies. Strikingly, here we demonstrate that bias still exists in the PAD of samples with the same age even after kind of correction. Therefore, current PAD may not be taken as a reliable phenotype and more investigations are needed to solve this fundamental defect. To this end, we propose an age-level bias correction method and demonstrate its efficacy in numerical experiments.