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Haifeng Xia

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7 papers

2 author rows

NeurIPS Conference 2025 Conference Paper

Rethinking Joint Maximum Mean Discrepancy for Visual Domain Adaptation

Wei Wang
Haifeng Xia
Chao Huang
Zhengming Ding
Cong Wang
Haojie Li
Xiaochun Cao

In domain adaption (DA), joint maximum mean discrepancy (JMMD), as a famous distribution-distance metric, aims to measure joint probability distribution difference between the source domain and target domain, while it is still not fully explored and especially hard to be applied into a subspace-learning framework as its empirical estimation involves a tensor-product operator whose partial derivative is difficult to obtain. To solve this issue, we deduce a concise JMMD based on the Representer theorem that avoids the tensor-product operator and obtains two essential findings. First, we reveal the uniformity of JMMD by proving that previous marginal, class conditional, and weighted class conditional probability distribution distances are three special cases of JMMD with different label reproducing kernels. Second, inspired by graph embedding, we observe that the similarity weights, which strengthen the intra-class compactness in the graph of Hilbert Schmidt independence criterion (HSIC), take opposite signs in the graph of JMMD, revealing why JMMD degrades the feature discrimination. This motivates us to propose a novel loss JMMD-HSIC by jointly considering JMMD and HSIC to promote discrimination of JMMD. Extensive experiments on several cross-domain datasets could demonstrate the validity of our revealed theoretical results and the effectiveness of our proposed JMMD-HSIC.

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ICRA Conference 2025 Conference Paper

RoBiFusion: A Robust and Bidirectional Interaction Camera-LiDAR 3D Object Detection Framework

Xubin Wen
Haifeng Xia
Zhengming Ding
Siyu Xia

Camera-LiDAR 3D object detection is currently becoming a crucial component in the field of autonomous driving perception. However, previous models only performed feature fusion in the deep-level BEV hierarchy when dealing with camera-LiDAR feature fusion. This approach lacks interaction with the shallow-level sensor features, which is beneficial in constructing the corresponding BEV features. However, a simple shallow-level feature interaction can introduce sensor noise caused by intrinsic and extrinsic camera calibration errors. To address this, we propose RoBiFusion, a novel camera-LiDAR 3D object detection framework designed for effective sensor feature interaction and mitigating sensor noise interference. This framework consists of three submodules: the Camera-LiDAR Feature Matching module, the LiDAR-to-Camera module, and the Camera-to-LiDAR module. Firstly, in the Camera-LiDAR Feature Matching module, we use the cross-attention module to dynamically match the camera features and the LiDAR features, which solves the problem of feature inconsistency caused by noise in the camera's intrinsic and extrinsic parameters. Secondly, in the LiDAR-to-Camera module, we propose a novel depth representation that can effectively mitigate LiDAR noise interference. Thirdly, in the Camera-to-LiDAR module, we introduce deformable attention to help LiDAR feature capture instance-level semantic features. Additionally, we design a novel differentiable and efficient grid sample module to accelerate the process since the bilinear grid sample module in deformable attention is time-consuming and not deployment-friendly. We compared RoBiFusion to the state-of-the-art BEVFusion on the nuScenes dataset and found that RoBiFusion surpasses BEVFusion by 1. 5% mAP and 2. 4% NDS. Furthermore, we designed a series of ablation experiments to verify the effectiveness of the aforementioned modules.

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AAAI Conference 2025 Conference Paper

Supportive Negatives Spectral Augmentation for Source-Free Cross-Domain Segmentation

Kexin Zheng
Haifeng Xia
Siyu Xia
Ming Shao
Zhengming Ding

Source-free domain adaptation (SFDA) aims to transfer knowledge from the well-trained source model and optimize it to adapt target data distribution. SFDA methods are suitable for medical image segmentation task due to its data-privacy protection and achieve promising performances. However, cross-domain distribution shift makes it difficult for the adapted model to provide accurate decisions on several hard instances and negatively affects model generalization. To overcome this limitation, a novel method `supportive negatives spectral augmentation' (SNSA) is presented in this work. Concretely, SNSA includes the instance selection mechanism to automatically discover a few hard samples for which source model produces incorrect predictions. And, active learning strategy is adopted to re-calibrate their predictive masks. Moreover, SNSA deploys the spectral augmentation between hard instances and others to encourage source model to gradually capture and adapt the attributions of target distribution. Considerable experimental studies demonstrate that annotating merely 4%~5% of negative instances from the target domain significantly improves segmentation performance over previous methods.

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IJCAI Conference 2022 Conference Paper

Adversarial Bi-Regressor Network for Domain Adaptive Regression

Haifeng Xia
Pu Wang
Toshiaki Koike-Akino
Ye Wang
Philip Orlik
Zhengming Ding

Domain adaptation (DA) aims to transfer the knowledge of a well-labeled source domain to facilitate unlabeled target learning. When turning to specific tasks such as indoor (Wi-Fi) localization, it is essential to learn a cross-domain regressor to mitigate the domain shift. This paper proposes a novel method Adversarial Bi-Regressor Network (ABRNet) to seek more effective cross- domain regression model. Specifically, a discrepant bi-regressor architecture is developed to maximize the difference of bi-regressor to discover uncertain target instances far from the source distribution, and then an adversarial training mechanism is adopted between feature extractor and dual regressors to produce domain-invariant representations. To further bridge the large domain gap, a domain- specific augmentation module is designed to synthesize two source-similar and target-similar inter- mediate domains to gradually eliminate the original domain mismatch. The empirical studies on two cross-domain regressive benchmarks illustrate the power of our method on solving the domain adaptive regression (DAR) problem.

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AAAI Conference 2022 Conference Paper

Cross-Domain Collaborative Normalization via Structural Knowledge

Haifeng Xia
Zhengming Ding

Batch Normalization (BN) as an important component assists Deep Neural Networks in achieving promising performance for extensive learning tasks by scaling distribution of feature representations within mini-batches. However, the application of BN suffers from performance degradation under the scenario of Unsupervised Domain Adaptation (UDA), since the estimated statistics fail to concurrently describe two different domains. In this paper, we develop a novel normalization technique, named Collaborative Normalization (CoN), for eliminating domain discrepancy and accelerating the model training of neural networks for UDA. Unlike typical strategies only exploiting domain-specific statistics during normalization, our CoN excavates cross-domain knowledge and simultaneously scales features from various domains by mimicking the merits of collaborative representation. Our CoN can be easily plugged into popular neural network backbones for cross-domain learning. On the one hand, theoretical analysis guarantees that models with CoN promote discriminability of feature representations and accelerate convergence rate; on the other hand, empirical study verifies that replacing BN with CoN in popular network backbones effectively improves classification accuracy in most learning tasks across three cross-domain visual benchmarks.

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AAAI Conference 2020 Conference Paper

Bi-Directional Generation for Unsupervised Domain Adaptation

Guanglei Yang
Haifeng Xia
Mingli Ding
Zhengming Ding

Unsupervised domain adaptation facilitates the unlabeled target domain relying on well-established source domain information. The conventional methods forcefully reducing the domain discrepancy in the latent space will result in the destruction of intrinsic data structure. To balance the mitigation of domain gap and the preservation of the inherent structure, we propose a Bi-Directional Generation domain adaptation model with consistent classiﬁers interpolating two intermediate domains to bridge source and target domains. Speciﬁcally, two cross-domain generators are employed to synthesize one domain conditioned on the other. The performance of our proposed method can be further enhanced by the consistent classiﬁers and the cross-domain alignment constraints. We also design two classiﬁers which are jointly optimized to maximize the consistency on target sample prediction. Extensive experiments verify that our proposed model outperforms the state-of-the-art on standard cross domain visual benchmarks.

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NeurIPS Conference 2016 Conference Paper

Error Analysis of Generalized Nyström Kernel Regression

Hong Chen
Haifeng Xia
Heng Huang
Weidong Cai

Nystr\"{o}m method has been used successfully to improve the computational efficiency of kernel ridge regression (KRR). Recently, theoretical analysis of Nystr\"{o}m KRR, including generalization bound and convergence rate, has been established based on reproducing kernel Hilbert space (RKHS) associated with the symmetric positive semi-definite kernel. However, in real world applications, RKHS is not always optimal and kernel function is not necessary to be symmetric or positive semi-definite. In this paper, we consider the generalized Nystr\"{o}m kernel regression (GNKR) with $\ell_2$ coefficient regularization, where the kernel just requires the continuity and boundedness. Error analysis is provided to characterize its generalization performance and the column norm sampling is introduced to construct the refined hypothesis space. In particular, the fast learning rate with polynomial decay is reached for the GNKR. Experimental analysis demonstrates the satisfactory performance of GNKR with the column norm sampling.

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