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Binhui Xie

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9 papers

2 author rows

AAAI Conference 2026 Conference Paper

SPSC: Sparse and Scalable Multi-Modal 3D Occupancy Prediction for Autonomous Driving

Qingju Guo
Shuang Li
Binhui Xie
Jing Geng
Wei Li

3D semantic occupancy prediction offers a nuanced representation of the surrounding environment, which is crucial for ensuring the safety of autonomous driving. However, fine-grained scene representations inevitably result in cubic growth in data scale, which imposes substantial demands on model architecture and computational complexity, especially in high-resolution scenarios. Existing approaches for handling high-resolution scenes typically obtain fine-grained features by grid sampling on low-resolution feature map, resulting in limited sparsity and insufficient feature interaction. This paper presents a framework leveraging SParse representation and SCalable feature interaction to address the aforementioned challenges, called SPSC. Specifically, we maintain sparsity by progressively pruning unoccupied queries during the coarse-to-fine process, thereby reducing the scale of data that the model needs to handle. Subsequently, we introduce query serialization, which transforms queries into an ordered sequence while preserving their spatial structure, This enables fine-grained feature interaction while maintaining linear computational complexity and a larger receptive field. Without complex architectural designs, SPSC significantly outperforms SOTA approaches, relatively enhances the mIoU by 12.0%, 11.0% and 4.8% on nuScenes-Occupancy dataset under the muli-modal, LiDAR and camera settings, respectively.

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ICML Conference 2025 Conference Paper

From Local Details to Global Context: Advancing Vision-Language Models with Attention-Based Selection

Lincan Cai
Jingxuan Kang
Shuang Li 0008
Wenxuan Ma 0001
Binhui Xie
Zhida Qin
Jian Liang 0002

Pretrained vision-language models (VLMs), e. g. , CLIP, demonstrate impressive zero-shot capabilities on downstream tasks. Prior research highlights the crucial role of visual augmentation techniques, like random cropping, in alignment with fine-grained class descriptions generated by large language models (LLMs), significantly enhancing zero-shot performance by incorporating multi-view information. However, the inherent randomness of these augmentations can inevitably introduce background artifacts and cause models to overly focus on local details, compromising global semantic understanding. To address these issues, we propose an A ttention- B ased S election ( ABS ) method from local details to global context, which applies attention-guided cropping in both raw images and feature space, supplement global semantic information through strategic feature selection. Additionally, we introduce a soft matching technique to effectively filter LLM descriptions for better alignment. ABS achieves state-of-the-art performance on out-of-distribution generalization and zero-shot classification tasks. Notably, ABS is training-free and even rivals few-shot and test-time adaptation methods.

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ICML Conference 2024 Conference Paper

Enhancing Cross-Modal Fine-Tuning with Gradually Intermediate Modality Generation

Lincan Cai
Shuang Li 0008
Wenxuan Ma 0001
Jingxuan Kang
Binhui Xie
Zixun Sun
Chengwei Zhu

Large-scale pretrained models have proven immensely valuable in handling data-intensive modalities like text and image. However, fine-tuning these models for certain specialized modalities, such as protein sequence and cosmic ray, poses challenges due to the significant modality discrepancy and scarcity of labeled data. In this paper, we propose an end-to-end method, PaRe, to enhance cross-modal fine-tuning, aiming to transfer a large-scale pretrained model to various target modalities. PaRe employs a gating mechanism to select key patches from both source and target data. Through a modality-agnostic Pa tch Re placement scheme, these patches are preserved and combined to construct data-rich intermediate modalities ranging from easy to hard. By gradually intermediate modality generation, we can not only effectively bridge the modality gap to enhance stability and transferability of cross-modal fine-tuning, but also address the challenge of limited data in the target modality by leveraging enriched intermediate modality data. Compared with hand-designed, general-purpose, task-specific, and state-of-the-art cross-modal fine-tuning approaches, PaRe demonstrates superior performance across three challenging benchmarks, encompassing more than ten modalities.

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NeurIPS Conference 2024 Conference Paper

Weight Diffusion for Future: Learn to Generalize in Non-Stationary Environments

Mixue Xie
Shuang Li
Binhui Xie
Chi H. Liu
Jian Liang
Zixun Sun
Ke Feng
Chengwei Zhu

Enabling deep models to generalize in non-stationary environments is vital for real-world machine learning, as data distributions are often found to continually change. Recently, evolving domain generalization (EDG) has emerged to tackle the domain generalization in a time-varying system, where the domain gradually evolves over time in an underlying continuous structure. Nevertheless, it typically assumes multiple source domains simultaneously ready. It still remains an open problem to address EDG in the domain-incremental setting, where source domains are non-static and arrive sequentially to mimic the evolution of training domains. To this end, we propose Weight Diffusion (W-Diff), a novel framework that utilizes the conditional diffusion model in the parameter space to learn the evolving pattern of classifiers during the domain-incremental training process. Specifically, the diffusion model is conditioned on the classifier weights of different historical domain (regarded as a reference point) and the prototypes of current domain, to learn the evolution from the reference point to the classifier weights of current domain (regarded as the anchor point). In addition, a domain-shared feature encoder is learned by enforcing prediction consistency among multiple classifiers, so as to mitigate the overfitting problem and restrict the evolving pattern to be reflected in the classifier as much as possible. During inference, we adopt the ensemble manner based on a great number of target domain-customized classifiers, which are cheaply obtained via the conditional diffusion model, for robust prediction. Comprehensive experiments on both synthetic and real-world datasets show the superior generalization performance of W-Diff on unseen domains in the future.

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NeurIPS Conference 2023 Conference Paper

Annotator: A Generic Active Learning Baseline for LiDAR Semantic Segmentation

Binhui Xie
Shuang Li
Qingju Guo
Chi Liu
Xinjing Cheng

Active learning, a label-efficient paradigm, empowers models to interactively query an oracle for labeling new data. In the realm of LiDAR semantic segmentation, the challenges stem from the sheer volume of point clouds, rendering annotation labor-intensive and cost-prohibitive. This paper presents Annotator, a general and efficient active learning baseline, in which a voxel-centric online selection strategy is tailored to efficiently probe and annotate the salient and exemplar voxel girds within each LiDAR scan, even under distribution shift. Concretely, we first execute an in-depth analysis of several common selection strategies such as Random, Entropy, Margin, and then develop voxel confusion degree (VCD) to exploit the local topology relations and structures of point clouds. Annotator excels in diverse settings, with a particular focus on active learning (AL), active source-free domain adaptation (ASFDA), and active domain adaptation (ADA). It consistently delivers exceptional performance across LiDAR semantic segmentation benchmarks, spanning both simulation-to-real and real-to-real scenarios. Surprisingly, Annotator exhibits remarkable efficiency, requiring significantly fewer annotations, e. g. , just labeling five voxels per scan in the SynLiDAR → SemanticKITTI task. This results in impressive performance, achieving 87. 8% fully-supervised performance under AL, 88. 5% under ASFDA, and 94. 4% under ADA. We envision that Annotator will offer a simple, general, and efficient solution for label-efficient 3D applications.

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AAAI Conference 2023 Conference Paper

VBLC: Visibility Boosting and Logit-Constraint Learning for Domain Adaptive Semantic Segmentation under Adverse Conditions

Mingjia Li
Binhui Xie
Shuang Li
Chi Harold Liu
Xinjing Cheng

Generalizing models trained on normal visual conditions to target domains under adverse conditions is demanding in the practical systems. One prevalent solution is to bridge the domain gap between clear- and adverse-condition images to make satisfactory prediction on the target. However, previous methods often reckon on additional reference images of the same scenes taken from normal conditions, which are quite tough to collect in reality. Furthermore, most of them mainly focus on individual adverse condition such as nighttime or foggy, weakening the model versatility when encountering other adverse weathers. To overcome the above limitations, we propose a novel framework, Visibility Boosting and Logit-Constraint learning (VBLC), tailored for superior normal-toadverse adaptation. VBLC explores the potential of getting rid of reference images and resolving the mixture of adverse conditions simultaneously. In detail, we first propose the visibility boost module to dynamically improve target images via certain priors in the image level. Then, we figure out the overconfident drawback in the conventional cross-entropy loss for self-training method and devise the logit-constraint learning, which enforces a constraint on logit outputs during training to mitigate this pain point. To the best of our knowledge, this is a new perspective for tackling such a challenging task. Extensive experiments on two normal-to-adverse domain adaptation benchmarks, i.e., Cityscapes to ACDC and Cityscapes to FoggyCityscapes + RainCityscapes, verify the effectiveness of VBLC, where it establishes the new state of the art. Code is available at https://github.com/BIT-DA/VBLC.

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AAAI Conference 2022 Conference Paper

Active Learning for Domain Adaptation: An Energy-Based Approach

Binhui Xie
Longhui Yuan
Shuang Li
Chi Harold Liu
Xinjing Cheng
Guoren Wang

Unsupervised domain adaptation has recently emerged as an effective paradigm for generalizing deep neural networks to new target domains. However, there is still enormous potential to be tapped to reach the fully supervised performance. In this paper, we present a novel active learning strategy to assist knowledge transfer in the target domain, dubbed active domain adaptation. We start from an observation that energy-based models exhibit free energy biases when training (source) and test (target) data come from different distributions. Inspired by this inherent mechanism, we empirically reveal that a simple yet efficient energy-based sampling strategy sheds light on selecting the most valuable target samples than existing approaches requiring particular architectures or computation of the distances. Our algorithm, Energy-based Active Domain Adaptation (EADA), queries groups of target data that incorporate both domain characteristic and instance uncertainty into every selection round. Meanwhile, by aligning the free energy of target data compact around the source domain via a regularization term, domain gap can be implicitly diminished. Through extensive experiments, we show that EADA surpasses state-of-the-art methods on well-known challenging benchmarks with substantial improvements, making it a useful option in the open world. Code is available at https: //github. com/BIT-DA/EADA.

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AAAI Conference 2021 Conference Paper

Bi-Classifier Determinacy Maximization for Unsupervised Domain Adaptation

Shuang Li
Fangrui Lv
Binhui Xie
Chi Harold Liu
Jian Liang
Chen Qin

Unsupervised domain adaptation challenges the problem of transferring knowledge from a well-labelled source domain to an unlabelled target domain. Recently, adversarial learning with bi-classifier has been proven effective in pushing crossdomain distributions close. Prior approaches typically leverage the disagreement between bi-classifier to learn transferable representations, however, they often neglect the classifier determinacy in the target domain, which could result in a lack of feature discriminability. In this paper, we present a simple yet effective method, namely Bi-Classifier Determinacy Maximization (BCDM), to tackle this problem. Motivated by the observation that target samples cannot always be separated distinctly by the decision boundary, here in the proposed BCDM, we design a novel classifier determinacy disparity (CDD) metric, which formulates classifier discrepancy as the class relevance of distinct target predictions and implicitly introduces constraint on the target feature discriminability. To this end, the BCDM can generate discriminative representations by encouraging target predictive outputs to be consistent and determined, meanwhile, preserve the diversity of predictions in an adversarial manner. Furthermore, the properties of CDD as well as the theoretical guarantees of BCDM’s generalization bound are both elaborated. Extensive experiments show that BCDM compares favorably against the existing state-of-the-art domain adaptation methods.

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AAAI Conference 2020 Conference Paper

Domain Conditioned Adaptation Network

Shuang Li
Chi Liu
Qiuxia Lin
Binhui Xie
Zhengming Ding
Gao Huang
Jian Tang

Tremendous research efforts have been made to thrive deep domain adaptation (DA) by seeking domain-invariant features. Most existing deep DA models only focus on aligning feature representations of task-speciﬁc layers across domains while integrating a totally shared convolutional architecture for source and target. However, we argue that such strongly-shared convolutional layers might be harmful for domain-speciﬁc feature learning when source and target data distribution differs to a large extent. In this paper, we relax a shared-convnets assumption made by previous DA methods and propose a Domain Conditioned Adaptation Network (DCAN), which aims to excite distinct convolutional channels with a domain conditioned channel attention mechanism. As a result, the critical low-level domain-dependent knowledge could be explored appropriately. As far as we know, this is the ﬁrst work to explore the domain-wise convolutional channel activation for deep DA networks. Moreover, to effectively align high-level feature distributions across two domains, we further deploy domain conditioned feature correction blocks after task-speciﬁc layers, which will explicitly correct the domain discrepancy. Extensive experiments on three crossdomain benchmarks demonstrate the proposed approach outperforms existing methods by a large margin, especially on very tough cross-domain learning tasks.

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