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Pengju Ren

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6 papers

2 author rows

AAAI Conference 2026 Conference Paper

Double Rounding: Nearly Lossless Adaptive Bit Switching for QAT

Haiduo Huang
Zhenhua Liu
Tian Xia
Pengju Ren

Model quantization is widely applied for compressing and accelerating deep neural networks (DNNs). However, conventional Quantization-Aware Training (QAT) focuses on training DNNs with uniform bit-width. The bit-width settings vary across different hardware and transmission demands, which induces considerable training and storage costs. Hence, the scheme of one-shot joint training multiple precisions is proposed to address this issue. Previous works either store a larger FP32 model to switch between different precision models for higher accuracy or store a smaller INT8 model but compromise accuracy due to using shared quantization parameters. In this paper, we introduce the Double Rounding quantization method, which fully utilizes the quantized representation range to accomplish nearly lossless bit-switching while reducing storage by using the highest integer precision instead of full precision. Furthermore, we observe a competitive interference among different precisions during one-shot joint training, primarily due to inconsistent gradients of quantization scales during backward propagation. To tackle this problem, we propose an Adaptive Learning Rate Scaling (AdaScale) technique that dynamically adapts learning rates for various precisions to optimize the training process. Additionally, we extend our Double Rounding to one-shot mixed precision training and develop a Hessian-Aware Stochastic Bit-switching (HessBit) strategy. Experimental results on the ImageNet-1K classification demonstrate that our methods have enough advantages to state-of-the-art one-shot joint QAT in both multi-precision and mixed-precision. We validate the feasibility of our method on detection and segmentation tasks, as well as on LLMs task.

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AAAI Conference 2026 Conference Paper

PartialNet: Compute Less, Perform Better

Haiduo Huang
Tian Xia
Wenzhe Zhao
Pengju Ren

Achieving a balance between low parameter count, reduced FLOPs, and high accuracy and throughput remains a central challenge in neural network design. To address this, we propose the partial channel mechanism (PCM), which leverages the inherent redundancy in feature map channels. PCM divides feature map channels into multiple groups, each processed by distinct operations such as convolution, attention, pooling, or identity mapping. Building on this, we introduce partial attention convolution (PATConv), a novel module that efficiently fuses convolution and visual attention within a unified framework. Our results demonstrate that PATConv can fully replace both standard convolution and visual attention modules, leading to significant reductions in parameters and FLOPs. Furthermore, PATConv enables three efficient visual attention variants: Partial Channel Attention, Partial Spatial Attention, and Partial Self-Attention. To further optimize the allocation of channel splits, we propose dynamic {partial convolution (DPConv), which adaptively learns the optimal split ratio for each layer, achieving a better trade-off between speed and accuracy. By integrating PATConv and DPConv, we develop a new hybrid network family, PartialNet, which achieves superior top-1 accuracy and inference speed on ImageNet-1K, and demonstrates strong performance on COCO detection and segmentation tasks.

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NeurIPS Conference 2025 Conference Paper

DeepKD: A Deeply Decoupled and Denoised Knowledge Distillation Trainer

Haiduo Huang
Jiangcheng Song
Yadong Zhang
Pengju Ren

Recent advances in knowledge distillation have emphasized the importance of decoupling different knowledge components. While existing methods utilize momentum mechanisms to separate task-oriented and distillation gradients, they overlook the inherent conflict between target-class and non-target-class knowledge flows. Furthermore, low-confidence dark knowledge in non-target classes introduces noisy signals that hinder effective knowledge transfer. To address these limitations, we propose DeepKD, a novel training framework that integrates dual-level decoupling with adaptive denoising. First, through theoretical analysis of gradient signal-to-noise ratio (GSNR) characteristics in task-oriented and non-task-oriented knowledge distillation, we design independent momentum updaters for each component to prevent mutual interference. We observe that the optimal momentum coefficients for task-oriented gradient (TOG), target-class gradient (TCG), and non-target-class gradient (NCG) should be positively related to their GSNR. Second, we introduce a dynamic top-k mask (DTM) mechanism that gradually increases K from a small initial value to incorporate more non-target classes as training progresses, following curriculum learning principles. The DTM jointly filters low-confidence logits from both teacher and student models, effectively purifying dark knowledge during early training. Extensive experiments on CIFAR-100, ImageNet, and MS-COCO demonstrate DeepKD's effectiveness.

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NeurIPS Conference 2025 Conference Paper

GeGS-PCR: Fast and Robust Color 3D Point Cloud Registration with Two-Stage Geometric-3DGS Fusion

Jiayi Tian
Haiduo Huang
Tian Xia
Wenzhe Zhao
Pengju Ren

We address the challenge of point cloud registration using color information, where traditional methods relying solely on geometric features often struggle in low-overlap and incomplete scenarios. To overcome these limitations, we propose GeGS-PCR, a novel two-stage method that combines geometric, color, and Gaussian information for robust registration. Our approach incorporates a dedicated color encoder that enhances color features by extracting multi-level geometric and color data from the original point cloud. We introduce the Geometric-3DGS module, which encodes the local neighborhood information of colored superpoints to ensure a globally invariant geometric-color context. Leveraging LORA optimization, we maintain high performance while preserving the expressiveness of 3DGS. Additionally, fast differentiable rendering is utilized to refine the registration process, leading to improved convergence. To further enhance performance, we propose a joint photometric loss that exploits both geometric and color features. This enables strong performance in challenging conditions with extremely low point cloud overlap. We validate our method by colorizing the Kitti dataset as ColorKitti and testing on both Color3DMatch and Color3DLoMatch datasets. Our method achieves state-of-the-art performance with Registration Recall at 99. 9%, Relative Rotation Error as low as 0. 013, and Relative Translation Error as low as 0. 024, improving precision by at least a factor of 2.

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ICRA Conference 2025 Conference Paper

Rapid Dynamic Obstacle Avoidance for UAVs Enhanced by DVS and Neuromorphic Computing

Siyang Wang
Sheng Yu
Tingbang Liang
Yilin Shi
Yongqiang Ma
Pengju Ren

Achieving rapid and accurate dynamic obstacle avoidance is crucial for enhancing the survivability of unmanned aerial vehicles (UAVs) in hazardous conditions. To accomplish dynamic obstacle avoidance, sensors with high temporal resolution and efficient processing models are required. Dynamic vision sensors (DVS) fulfill the sensing requirements, while spiking neural networks (SNNs) address the processing demands. In this paper, we develop an end-to-end obstacle avoidance algorithm for UAVs using only a single monocular DVS as the sensor and further enhance accuracy and speed through our proposed mechanisms. The algorithm consists of three components: ego-motion compensation, an SNN model for movement analysis, and a force filter inspired by spiking neurons. In movement analysis, we propose the temporal potential pooling (TPP) and incremental event (EI) mechanisms to accelerate our SNN model. The real-flight experiments confirm that our algorithm achieves approximately 90% accuracy with a processing latency as low as 4ms on a GPU, surpassing state-of-the-art methods. Ablation studies show that the proposed method maintains high accuracy in movement detection while significantly reducing computational time. Our method operates in real-time, achieves high accuracy, and is feasible across a wide range of environments. Our code is available at https://github.com/AmperiaWang/oanet_s1 for reproducibility.

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ICRA Conference 2024 Conference Paper

Towards Optimal Lane-changing Coordination of CAVs in Multi-lane Mixed Traffic Scenarios

Yan Ding
Yijun Mao
Chongshan Jiao
Pengju Ren

Lane changing is a fundamental but challenging operation for moving vehicles. Connected and Automated Vehicles(CAVs) enable autonomous vehicles to cooperate with each other to accomplish the lane changing tasks, profiting from their communication ability. However, dispatching CAVs in mixed traffic remains difficult due to the stochastic behaviors and uncertain intentions of Human-Driven Vehicles(HDVs). To tackle this issue, this paper devises a coordination approach based on Conflict-Based Search(CBS) theory. Firstly, HDVs are accurately modeled as constraints to enable usage of CBS in the mixed traffic. Additionally, virtual goals are introduced to search CAVs’ priority and outlets along with path finding. Furthermore, we optimize the performance of CBS in dense traffic by defining the concept of following vehicles. Experiments show that performance is improved by utilizing new conflict prioritizing rules and a heuristic value calculation method that derived from following vehicles. Finally, we introduce grouping vehicles to extend the proposed method for solving extremely dense and large instances at a scale of more than one hundred without significant loss in efficiency.

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