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Zhenghan Chen

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11 papers

2 author rows

AAAI Conference 2025 Conference Paper

Image-to-video Adaptation with Outlier Modeling and Robust Self-learning

Junbao Zhuo
Shuhui Wang
Zhenghan Chen
Li Shen
Qingming Huang
Huimin Ma

The image-to-video adaptation task seeks to effectively harness both labeled images and unlabeled videos for achieving effective video recognition. The modality gap of the image and video modalities and the domain discrepancy across the two domains are the two essential challenges in this task. Existing methods reduce the domain discrepancy via close-set domain adaptation techniques, resulting in inaccurate domain alignment as there exist outlier target frames. To tackle this issue, we extend the vanilla classifier with outlier classes, where each outlier class responsible for capturing outlier frames for a specific class via batch nuclear norm maximization loss. We further propose a new loss by treating the source images apart from class c as instances from outlier class specific for c. As for the modality gap, existing methods usually utilize the pseudo labels obtained from an image-level adapted model to learn a video-level model. Rare efforts are dedicated to handling the noise in pseudo labels. We proposed a new metric based on label propagation consistency to select samples for training a better video-level model. Experiments on 3 benchmarks validating the effectiveness of our method.

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IROS Conference 2025 Conference Paper

Natural Humanoid Robot Locomotion with Generative Motion Prior

Haodong Zhang
Liang Zhang
Zhenghan Chen
Lu Chen
Yue Wang 0020
Rong Xiong

Natural and lifelike locomotion remains a fundamental challenge for humanoid robots to interact with human society. However, previous methods either neglect motion naturalness or rely on unstable and ambiguous style rewards. In this paper, we propose a novel Generative Motion Prior (GMP) that provides fine-grained motion-level supervision for the task of natural humanoid robot locomotion. To leverage natural human motions, we first employ whole-body motion retargeting to effectively transfer them to the robot. Subsequently, we train a generative model offline to predict future natural reference motions for the robot based on a conditional variational auto-encoder. During policy training, the generative motion prior serves as a frozen online motion generator, delivering precise and comprehensive supervision at the trajectory level, including joint angles and keypoint positions. The generative motion prior significantly enhances training stability and improves interpretability by offering detailed and dense guidance throughout the learning process. Experimental results in both simulation and real-world environments demonstrate that our method achieves superior motion naturalness compared to existing approaches. Project page can be found at https://sites.google.com/view/humanoid-gmp

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AAAI Conference 2025 Conference Paper

Reducing Divergence in Batch Normalization for Domain Adaptation

Ellen Yi-Ge
Mingjing Wu
Zhenghan Chen

The widespread adoption of Batch Normalization (BN) in contemporary deep neural architectures has demonstrated significant efficacy, particularly in the domain of Unsupervised Domain Adaptation (UDA) for cross-domain applications. Notwithstanding its success, extant BN variants often conflate source and target domain information within identical channels, potentially compromising transferability due to inter-domain feature misalignment. To address this limitation, we introduce Refined Batch Normalization (RBN), a novel normalization paradigm that leverages estimated shift to quantify discrepancies between estimated population statistics and their expected values. Our pivotal observation reveals that estimated shift can accumulate through BN stacking within the network, potentially degrading target domain performance. We elucidate how RBN mitigates this accumulation, thereby enhancing overall system efficacy. The practical implementation of this technique is realized through the RBNBlock, which supplants conventional BN with RBN in the bottleneck architecture of residual networks. Extensive empirical evaluation across diverse cross-domain benchmarks corroborates the superiority of RBN in augmenting inter-domain transferability. This perspective transcends immediate performance metrics, offering a foundational lens through which subsequent research can more deeply understand and refine the interplay between normalization strategies and domain adaptation.

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AAAI Conference 2025 Conference Paper

RETRACTED: GEONet: Global Enhancement and Optimization Network for Lane Detection

Suyang Xi
Yunhao Liu
Hong Ding
Mingshuo Wang
Zhenghan Chen
Xiaoxuan Liang

Lane detection plays a crucial role in autonomous driving systems, enabling vehicles to navigate safely and efficiently in complex environment. Despite significant advancements in recent years, accurate lane detection remains a challenging task, particularly in scenarios with occlusions, ambiguous lane markings, and diverse lighting conditions. In this paper, we propose the Global Enhancement and Optimization Network (GEONet) for lane detection, which is designed to refine both feature extraction and global feature transmission. Traditional approaches typically depend on deep convolutional layer stacks for global feature extraction, a process that often compromises inference speed and the precision of global feature representation. In contrast, GEONet introduces a novel and more effective methodology. We present the Global Feature Extraction Module (GFEM), which is specifically engineered to capture comprehensive global features with higher accuracy. Additionally, we introduce the Top-Tier Supplementary Module (TTSM), which enhances these features through a bottom-up approach, improving overall lane detection accuracy. To further bolster our framework, we incorporate Whitening Batch Normalization (WBN) and Whitening Contrastive Learning (WCL), which enhance feature robustness and ensure better generalization. In addition to our novel network design, we propose two new loss functions to enhance lane detection accuracy. The Generalized Rectangular Intersection over Union (GRIoU) Loss extends the predicted points into rectangles, optimizing overlap and smoothness of lane predictions.The Angle Loss accounts for angular differences between predicted and ground truth lanes, improving alignment and continuity. Experimental results demonstrate that our proposed method significantly outperforms current state-of-the-art lane detection techniques. Editorial Notes This article, which was published in Proceedings of the Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI 2025), has been retracted by agreement between the authors and the journal.

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IROS Conference 2024 Conference Paper

A Fast Motion and Foothold Planning Framework for Legged Robots on Discrete Terrain

Jiyu Yu
Dongqi Wang 0002
Zhenghan Chen
Ci Chen 0004
Shuangpeng Wu
Yue Wang 0020
Rong Xiong

Legged robot proved their capability to cross complex terrain in recent research, yet the autonomy of robots on discrete terrain still needs to be enhanced since it requires a full stack framework. This paper introduces a real-time motion and foothold planning framework tailored for legged robots navigating uneven terrains, such as stepping stones. Our approach addresses the critical challenges of determining feasible global paths and local footholds to enhance autonomous mobility across complex landscapes. By using a sampling-based global path planner integrated with terrain segmentation and the robot’s kinematic model, our framework swiftly generates viable navigation paths. Concurrently, it utilizes a Mixed Integer Programming (MIP) methodology for real-time foothold optimization, ensuring the robot’s stability and safety through dynamic terrain interaction. Finally, an execution layer including Model Predictive Control (MPC) and Whole-Body Control (WBC) generates the robots’ motion. Simulation and real-world experiments demonstrate that our framework improves legged robots’ adaptability on discrete terrains.

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ICLR Conference 2024 Conference Paper

DREAM: Dual Structured Exploration with Mixup for Open-set Graph Domain Adaption

Nan Yin
Mengzhu Wang
Zhenghan Chen
Li Shen 0008
Huan Xiong
Bin Gu 0001
Xiao Luo 0001

Recently, numerous graph neural network methods have been developed to tackle domain shifts in graph data. However, these methods presuppose that unlabeled target graphs belong to categories previously seen in the source domain. This assumption could not hold true for in-the-wild target graphs. In this paper, we delve deeper to explore a more realistic problem open-set graph domain adaptation. Our objective is to not only identify target graphs from new categories but also accurately classify remaining target graphs into their respective categories under domain shift and label scarcity. To solve this challenging problem, we introduce a new method named Dual Structured Exploration with Mixup (DREAM). DREAM incorporates a graph-level representation learning branch as well as a subgraph-enhanced branch, which jointly explores graph topological structures from both global and local viewpoints. To maximize the use of unlabeled target graphs, we train these two branches simultaneously using posterior regularization to enhance their inter-module consistency. To accommodate the open-set setting, we amalgamate dissimilar samples to generate virtual unknown samples belonging to novel classes. Moreover, to alleviate domain shift, we establish a k nearest neighbor-based graph-of-graphs and blend multiple neighbors of each sample to produce cross-domain virtual samples for inter-domain consistency learning. Extensive experiments validate the effectiveness of the proposed DREAM in comparison to various state-of-the-art approaches in different settings.

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AAAI Conference 2024 Conference Paper

Dynamic Spiking Graph Neural Networks

Nan Yin
Mengzhu Wang
Zhenghan Chen
Giulia De Masi
Huan Xiong
Bin Gu

The integration of Spiking Neural Networks (SNNs) and Graph Neural Networks (GNNs) is gradually attracting attention due to the low power consumption and high efficiency in processing the non-Euclidean data represented by graphs. However, as a common problem, dynamic graph representation learning faces challenges such as high complexity and large memory overheads. Current work often uses SNNs instead of Recurrent Neural Networks (RNNs) by using binary features instead of continuous ones for efficient training, which overlooks graph structure information and leads to the loss of details during propagation. Additionally, optimizing dynamic spiking models typically requires the propagation of information across time steps, which increases memory requirements. To address these challenges, we present a framework named Dynamic Spiking Graph Neural Networks (Dy-SIGN). To mitigate the information loss problem, Dy-SIGN propagates early-layer information directly to the last layer for information compensation. To accommodate the memory requirements, we apply the implicit differentiation on the equilibrium state, which does not rely on the exact reverse of the forward computation. While traditional implicit differentiation methods are usually used for static situations, Dy-SIGN extends it to the dynamic graph setting. Extensive experiments on three large-scale real-world dynamic graph datasets validate the effectiveness of Dy-SIGN on dynamic node classification tasks with lower computational costs.

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AAAI Conference 2024 Conference Paper

GSENet:Global Semantic Enhancement Network for Lane Detection

Junhao Su
Zhenghan Chen
Chenghao He
Dongzhi Guan
Changpeng Cai
Tongxi Zhou
Jiashen Wei
Wenhua Tian

Lane detection is the cornerstone of autonomous driving. Although existing methods have achieved promising results, there are still limitations in addressing challenging scenarios such as abnormal weather, occlusion, and curves. These scenarios with low visibility usually require to rely on the broad information of the entire scene provided by global semantics and local texture information to predict the precise position and shape of the lane lines. In this paper, we propose a Global Semantic Enhancement Network for lane detection, which involves a complete set of systems for feature extraction and global features transmission. Traditional methods for global feature extraction usually require deep convolution layer stacks. However, this approach of obtaining global features solely through a larger receptive field not only fails to capture precise global features but also leads to an overly deep model, which results in slow inference speed. To address these challenges, we propose a novel operation called the Global feature Extraction Module (GEM). Additionally, we introduce the Top Layer Auxiliary Module (TLAM) as a channel for feature distillation, which facilitates a bottom-up transmission of global features. Furthermore, we introduce two novel loss functions: the Angle Loss, which account for the angle between predicted and ground truth lanes, and the Generalized Line IoU Loss function that considers the scenarios where significant deviations occur between the prediction of lanes and ground truth in some harsh conditions. The experimental results reveal that the proposed method exhibits remarkable superiority over the current state-of-the-art techniques for lane detection.Our codes are available at:https://github.com/crystal250/GSENet.

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AAAI Conference 2024 Conference Paper

Sharpness-Aware Model-Agnostic Long-Tailed Domain Generalization

Houcheng Su
Weihao Luo
Daixian Liu
Mengzhu Wang
Jing Tang
Junyang Chen
Cong Wang
Zhenghan Chen

Domain Generalization (DG) aims to improve the generalization ability of models trained on a specific group of source domains, enabling them to perform well on new, unseen target domains. Recent studies have shown that methods that converge to smooth optima can enhance the generalization performance of supervised learning tasks such as classification. In this study, we examine the impact of smoothness-enhancing formulations on domain adversarial training, which combines task loss and adversarial loss objectives. Our approach leverages the fact that converging to a smooth minimum with respect to task loss can stabilize the task loss and lead to better performance on unseen domains. Furthermore, we recognize that the distribution of objects in the real world often follows a long-tailed class distribution, resulting in a mismatch between machine learning models and our expectations of their performance on all classes of datasets with long-tailed class distributions. To address this issue, we consider the domain generalization problem from the perspective of the long-tail distribution and propose using the maximum square loss to balance different classes which can improve model generalizability. Our method's effectiveness is demonstrated through comparisons with state-of-the-art methods on various domain generalization datasets. Code: https://github.com/bamboosir920/SAMALTDG.

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AAAI Conference 2024 Conference Paper

Sparse Enhanced Network: An Adversarial Generation Method for Robust Augmentation in Sequential Recommendation

Junyang Chen
Guoxuan Zou
Pan Zhou
Wu Yirui
Zhenghan Chen
Houcheng Su
Huan Wang
Zhiguo Gong

Sequential Recommendation plays a significant role in daily recommendation systems, such as e-commerce platforms like Amazon and Taobao. However, even with the advent of large models, these platforms often face sparse issues in the historical browsing records of individual users due to new users joining or the introduction of new products. As a result, existing sequence recommendation algorithms may not perform well. To address this, sequence-based data augmentation methods have garnered attention. Existing sequence enhancement methods typically rely on augmenting existing data, employing techniques like cropping, masking prediction, random reordering, and random replacement of the original sequence. While these methods have shown improvements, they often overlook the exploration of the deep embedding space of the sequence. To tackle these challenges, we propose a Sparse Enhanced Network (SparseEnNet), which is a robust adversarial generation method. SparseEnNet aims to fully explore the hidden space in sequence recommendation, generating more robust enhanced items. Additionally, we adopt an adversarial generation method, allowing the model to differentiate between data augmentation categories and achieve better prediction performance for the next item in the sequence. Experiments have demonstrated that our method achieves a remarkable 4-14% improvement over existing methods when evaluated on the real-world datasets. (https://github.com/junyachen/SparseEnNet)

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AAAI Conference 2024 Conference Paper

UniADS: Universal Architecture-Distiller Search for Distillation Gap

Liming Lu
Zhenghan Chen
Xiaoyu Lu
Yihang Rao
Lujun Li
Shuchao Pang

In this paper, we present UniADS, the first Universal Architecture-Distiller Search framework for co-optimizing student architecture and distillation policies. Teacher-student distillation gap limits the distillation gains. Previous approaches seek to discover the ideal student architecture while ignoring distillation settings. In UniADS, we construct a comprehensive search space encompassing an architectural search for student models, knowledge transformations in distillation strategies, distance functions, loss weights, and other vital settings. To efficiently explore the search space, we utilize the NSGA-II genetic algorithm for better crossover and mutation configurations and employ the Successive Halving algorithm for search space pruning, resulting in improved search efficiency and promising results. Extensive experiments are performed on different teacher-student pairs using CIFAR-100 and ImageNet datasets. The experimental results consistently demonstrate the superiority of our method over existing approaches. Furthermore, we provide a detailed analysis of the search results, examining the impact of each variable and extracting valuable insights and practical guidance for distillation design and implementation.

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