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Junhui Hou

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27 papers

2 author rows

AAAI Conference 2026 Conference Paper

DiCaP: Distribution-Calibrated Pseudo-labeling for Semi-Supervised Multi-Label Learning

Bo Han
Zhuoming Li
Xiaoyu Wang
Yaxin Hou
Hui Liu
Junhui Hou
Yuheng Jia

Semi-supervised multi-label learning (SSMLL) aims to address the challenge of limited labeled data in multi-label learning (MLL) by leveraging unlabeled data to improve the model’s performance. While pseudo-labeling has become a dominant strategy in SSMLL, most existing methods assign equal weights to all pseudo-labels regardless of their quality, which can amplify the impact of noisy or uncertain predictions and degrade the overall performance. In this paper, we theoretically verify that the optimal weight for a pseudo-label should reflect its correctness likelihood. Empirically, we observe that on the same dataset, the correctness likelihood distribution of unlabeled data remains stable, even as the number of labeled training samples varies. Building on this insight, we propose Distribution-Calibrated Pseudo-labeling (DiCaP), a correctness-aware framework that estimates posterior precision to calibrate pseudo-label weights. We further introduce a dual-thresholding mechanism to separate confident and ambiguous regions: confident samples are pseudo-labeled and weighted accordingly, while ambiguous ones are explored by unsupervised contrastive learning. Experiments conducted on multiple benchmark datasets verify that our method achieves consistent improvements, surpassing state-of-the-art methods by up to 4.27%.

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AAAI Conference 2026 Conference Paper

ESMC: MLLM-Based Embedding Selection for Explainable Multiple Clustering

Xinyue Wang
Yuheng Jia
Hui Liu
Junhui Hou

Typical deep clustering methods, while achieving notable progress, can only provide one clustering result per dataset. This limitation arises from their assumption of a fixed underlying data distribution, which may fail to meet user needs and provide unsatisfactory clustering outcomes. Our work investigates how multi-modal large language models (MLLMs) can be leveraged to achieve user-driven clustering, emphasizing their adaptability to user-specified semantic requirements. However, directly using MLLM output for clustering has risks for producing unstructured and generic image descriptions instead of feature-specific and concrete ones. To address these issues, our method first discovers that MLLMs' hidden states of text tokens are strongly related to the corresponding features, and leverages these embeddings to perform clusterings from any user-defined criteria. We also employ a lightweight clustering head augmented with pseudo-label learning, significantly enhancing clustering accuracy. Extensive experiments demonstrate its competitive performance on diverse datasets and metrics.

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AAAI Conference 2026 Conference Paper

Towards Better IncomLDL: We Are Unaware of Hidden Labels in Advance

Jiecheng Jiang
Jiawei Tang
Jiahao Jiang
Hui Liu
Junhui Hou
Yuheng Jia

Label distribution learning (LDL) is a novel paradigm that describe the samples by label distribution of a sample. However, acquiring LDL dataset is costly and time-consuming, which leads to the birth of incomplete label distribution learning (IncomLDL). All the previous IncomLDL methods set the description degrees of "missing" labels in an instance to 0, but remains those of other labels unchanged. This setting is unrealistic because when certain labels are missing, the degrees of the remaining labels will increase accordingly. We fix this unrealistic setting in IncomLDL and raise a new problem: LDL with hidden labels (HidLDL), which aims to recover a complete label distribution from a real-world incomplete label distribution where certain labels in an instance are omitted during annotation. To solve this challenging problem, we discover the significance of proportional information of the observed labels and capture it by an innovative constraint to utilize it during the optimization process. We simultaneously use local feature similarity and the global low-rank structure to reveal the mysterious veil of hidden labels. Moreover, we **theoretically** give the recovery bound of our method, proving the feasibility of our method in learning from hidden labels. Extensive recovery and predictive experiments on various datasets prove the superiority of our method to state-of-the-art LDL and IncomLDL methods.

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NeurIPS Conference 2025 Conference Paper

DOVTrack: Data-Efficient Open-Vocabulary Tracking

Zekun Qian
Ruize Han
Zhixiang Wang
Junhui Hou
Wei Feng

Open-Vocabulary Multi-Object Tracking (OVMOT) aims to detect and track multi-category objects including both seen and unseen categories during training. Currently, a significant challenge in this domain is the lack of large-scale annotated video data for training. To address this challenge, this work aims to effectively train the OV tracker using only the existing limited and sparsely annotated video data. We propose a comprehensive training sample space expansion strategy that addresses the fundamental limitation of sparse annotations in OVMOT training. Specifically, for the association task, we develop a diffusion-based feature generation framework that synthesizes intermediate object features between sparsely annotated frames, effectively expanding the training sample space by approximately 3× and enabling robust association learning from temporally continuous features. For the detection task, we introduce a dynamic group contrastive learning approach that generates diverse sample groups through affinity, dispersion, and adversarial grouping strategies, tripling the effective training samples for classification while maintaining sample quality. Additionally, we propose an adaptive localization loss that expands positive sample coverage by lowering IoU thresholds while mitigating noise through confidence-based weighting. Extensive experiments demonstrate that our method achieves state-of-the-art performance on the OVMOT benchmark, surpassing existing methods by 3. 8\% in TETA metric, without requiring additional data or annotations. The code will be available at https: //github. com/zekunqian/DOVTrack.

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ICML Conference 2025 Conference Paper

Generalization Performance of Ensemble Clustering: From Theory to Algorithm

Xu Zhang
Haoye Qiu
Weixuan Liang
Hui Liu 0032
Junhui Hou
Yuheng Jia

Ensemble clustering has demonstrated great success in practice; however, its theoretical foundations remain underexplored. This paper examines the generalization performance of ensemble clustering, focusing on generalization error, excess risk and consistency. We derive a convergence rate of generalization error bound and excess risk bound both of $\mathcal{O}(\sqrt{\frac{\log n}{m}}+\frac{1}{\sqrt{n}})$, with $n$ and $m$ being the numbers of samples and base clusterings. Based on this, we prove that when $m$ and $n$ approach infinity and $m$ is significantly larger than log $n$, i. e. , $m, n\to \infty, m\gg \log n$, ensemble clustering is consistent. Furthermore, recognizing that $n$ and $m$ are finite in practice, the generalization error cannot be reduced to zero. Thus, by assigning varying weights to finite clusterings, we minimize the error between the empirical average clusterings and their expectation. From this, we theoretically demonstrate that to achieve better clustering performance, we should minimize the deviation (bias) of base clustering from its expectation and maximize the differences (diversity) among various base clusterings. Additionally, we derive that maximizing diversity is nearly equivalent to a robust (min-max) optimization model. Finally, we instantiate our theory to develop a new ensemble clustering algorithm. Compared with SOTA methods, our approach achieves average improvements of 6. 0%, 7. 3%, and 6. 0% on 10 datasets w. r. t. NMI, ARI, and Purity. The code is available at https: //github. com/xuz2019/GPEC.

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NeurIPS Conference 2025 Conference Paper

Keep It on a Leash: Controllable Pseudo-label Generation Towards Realistic Long-Tailed Semi-Supervised Learning

Yaxin Hou
Bo Han
Yuheng Jia
Hui Liu
Junhui Hou

Current long-tailed semi-supervised learning methods assume that labeled data exhibit a long-tailed distribution, and unlabeled data adhere to a typical predefined distribution (i. e. , long-tailed, uniform, or inverse long-tailed). However, the distribution of the unlabeled data is generally unknown and may follow an arbitrary distribution. To tackle this challenge, we propose a Controllable Pseudo-label Generation (CPG) framework, expanding the labeled dataset with the progressively identified reliable pseudo-labels from the unlabeled dataset and training the model on the updated labeled dataset with a known distribution, making it unaffected by the unlabeled data distribution. Specifically, CPG operates through a controllable self-reinforcing optimization cycle: (i) at each training step, our dynamic controllable filtering mechanism selectively incorporates reliable pseudo-labels from the unlabeled dataset into the labeled dataset, ensuring that the updated labeled dataset follows a known distribution; (ii) we then construct a Bayes-optimal classifier using logit adjustment based on the updated labeled data distribution; (iii) this improved classifier subsequently helps identify more reliable pseudo-labels in the next training step. We further theoretically prove that this optimization cycle can significantly reduce the generalization error under some conditions. Additionally, we propose a class-aware adaptive augmentation module to further improve the representation of minority classes, and an auxiliary branch to maximize data utilization by leveraging all labeled and unlabeled samples. Comprehensive evaluations on various commonly used benchmark datasets show that CPG achieves consistent improvements, surpassing state-of-the-art methods by up to 15. 97\% in accuracy. The code is available at https: //github. com/yaxinhou/CPG.

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ICML Conference 2025 Conference Paper

Learning from Sample Stability for Deep Clustering

Zhixin Li
Yuheng Jia
Hui Liu 0032
Junhui Hou

Deep clustering, an unsupervised technique independent of labels, necessitates tailored supervision for model training. Prior methods explore supervision like similarity and pseudo labels, yet overlook individual sample training analysis. Our study correlates sample stability during unsupervised training with clustering accuracy and network memorization on a per-sample basis. Unstable representations across epochs often lead to mispredictions, indicating difficulty in memorization and atypicality. Leveraging these findings, we introduce supervision signals for the first time based on sample stability at the representation level. Our proposed strategy serves as a versatile tool to enhance various deep clustering techniques. Experiments across benchmark datasets showcase that incorporating sample stability into training can improve the performance of deep clustering. The code is available at https: //github. com/LZX-001/LFSS.

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ICLR Conference 2025 Conference Paper

MoDGS: Dynamic Gaussian Splatting from Casually-captured Monocular Videos with Depth Priors

Qingming Liu
Yuan Liu 0025
Jiepeng Wang 0001
Xianqiang Lyu
Peng Wang 0099
Wenping Wang 0001
Junhui Hou

In this paper, we propose MoDGS, a new pipeline to render novel-view images in dynamic scenes using only casually captured monocular videos. Previous monocular dynamic NeRF or Gaussian Splatting methods strongly rely on the rapid movement of input cameras to construct multiview consistency but fail to reconstruct dynamic scenes on casually captured input videos whose cameras are static or move slowly. To address this challenging task, MoDGS adopts recent single-view depth estimation methods to guide the learning of the dynamic scene. Then, a novel 3D-aware initialization method is proposed to learn a reasonable deformation field and a new robust depth loss is proposed to guide the learning of dynamic scene geometry. Comprehensive experiments demonstrate that MoDGS is able to render high-quality novel view images of dynamic scenes from just a casually captured monocular video, which outperforms baseline methods by a significant margin. Project page: https://MoDGS.github.io

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ICLR Conference 2025 Conference Paper

NVS-Solver: Video Diffusion Model as Zero-Shot Novel View Synthesizer

Meng You
Zhiyu Zhu
Hui Liu 0032
Junhui Hou

By harnessing the potent generative capabilities of pre-trained large video diffusion models, we propose a new novel view synthesis paradigm that operates without the need for training. The proposed method adaptively modulates the diffusion sampling process with the given views to enable the creation of visually pleasing results from single or multiple views of static scenes or monocular videos of dynamic scenes. Specifically, built upon our theoretical modeling, we iteratively modulate the score function with the given scene priors represented with warped input views to control the video diffusion process. Moreover, by theoretically exploring the boundary of the estimation error, we achieve the modulation in an adaptive fashion according to the view pose and the number of diffusion steps. Extensive evaluations on both static and dynamic scenes substantiate the significant superiority of our method over state-of-the-art methods both quantitatively and qualitatively. The source code can be found on https://github.com/ZHU-Zhiyu/NVS_Solver.

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ICLR Conference 2025 Conference Paper

ParaSolver: A Hierarchical Parallel Integral Solver for Diffusion Models

Jianrong Lu
Zhiyu Zhu
Junhui Hou

This paper explores the challenge of accelerating the sequential inference process of Diffusion Probabilistic Models (DPMs). We tackle this critical issue from a dynamic systems perspective, in which the inherent sequential nature is transformed into a parallel sampling process. Specifically, we propose a unified framework that generalizes the sequential sampling process of DPMs as solving a system of banded nonlinear equations. Under this generic framework, we reveal that the Jacobian of the banded nonlinear equations system possesses a unit-diagonal structure, enabling further approximation for acceleration. Moreover, we theoretically propose an effective initialization approach for parallel sampling methods. Finally, we construct \textit{ParaSolver}, a hierarchical parallel sampling technique that enhances sampling speed without compromising quality. Extensive experiments show that ParaSolver achieves up to \textbf{12.1× speedup} in terms of wall-clock time. The source code is publicly available at https://github.com/Jianrong-Lu/ParaSolver.git.

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ICLR Conference 2025 Conference Paper

Shape as Line Segments: Accurate and Flexible Implicit Surface Representation

Siyu Ren
Junhui Hou

Distance field-based implicit representations like signed/unsigned distance fields have recently gained prominence in geometry modeling and analysis. However, these distance fields are reliant on the closest distance of points to the surface, introducing inaccuracies when interpolating along cube edges during surface extraction. Additionally, their gradients are ill-defined at certain locations, causing distortions in the extracted surfaces. To address this limitation, we propose Shape as Line Segments (SALS), an accurate and efficient implicit geometry representation based on attributed line segments, which can handle arbitrary structures. Unlike previous approaches, SALS leverages a differentiable Line Segment Field to implicitly capture the spatial relationship between line segments and the surface. Each line segment is associated with two key attributes, intersection flag and ratio, from which we propose edge-based dual contouring to extract a surface. We further implement SALS with a neural network, producing a new neural implicit presentation. Additionally, based on SALS, we design a novel learning-based pipeline for reconstructing surfaces from 3D point clouds. We conduct extensive experiments, showcasing the significant advantages of our methods over state-of-the-art methods. The source code is available at https://github.com/rsy6318/SALS.

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ICLR Conference 2025 Conference Paper

Towards Calibrated Deep Clustering Network

Yuheng Jia
Jianhong Cheng
Hui Liu 0032
Junhui Hou

Deep clustering has exhibited remarkable performance; however, the over confidence problem, i.e., the estimated confidence for a sample belonging to a particular cluster greatly exceeds its actual prediction accuracy, has been over looked in prior research. To tackle this critical issue, we pioneer the development of a calibrated deep clustering framework. Specifically, we propose a novel dual head (calibration head and clustering head) deep clustering model that can effectively calibrate the estimated confidence and the actual accuracy. The calibration head adjusts the overconfident predictions of the clustering head, generating prediction confidence that matches the model learning status. Then, the clustering head dynamically selects reliable high-confidence samples estimated by the calibration head for pseudo-label self-training. Additionally, we introduce an effective network initialization strategy that enhances both training speed and network robustness. The effectiveness of the proposed calibration approach and initialization strategy are both endorsed with solid theoretical guarantees. Extensive experiments demonstrate the proposed calibrated deep clustering model not only surpasses the state-of-the-art deep clustering methods by 5× on average in terms of expected calibration error, but also significantly outperforms them in terms of clustering accuracy. The code is available at https://github.com/ChengJianH/CDC.

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NeurIPS Conference 2025 Conference Paper

You Can Trust Your Clustering Model: A Parameter-free Self-Boosting Plug-in for Deep Clustering

Hanyang Li
Yuheng Jia
Hui Liu
Junhui Hou

Recent deep clustering models have produced impressive clustering performance. However, a common issue with existing methods is the disparity between global and local feature structures. While local structures typically show strong consistency and compactness within class samples, global features often present intertwined boundaries and poorly separated clusters. Motivated by this observation, we propose **DCBoost, a parameter-free plug-in** designed to enhance the global feature structures of current deep clustering models. By harnessing reliable local structural cues, our method aims to elevate clustering performance effectively. Specifically, we first identify high-confidence samples through adaptive $k$-nearest neighbors-based consistency filtering, aiming to select a sufficient number of samples with high label reliability to serve as trustworthy anchors for self-supervision. Subsequently, these samples are utilized to compute a discriminative loss, which promotes both intra-class compactness and inter-class separability, to guide network optimization. Extensive experiments across various benchmark datasets showcase that our DCBoost significantly improves the clustering performance of diverse existing deep clustering models. Notably, our method improves the performance of current state-of-the-art baselines (e. g. , ProPos) by more than 3\% and amplifies the silhouette coefficient by over $7\times$. **Code is available at [https: //github. com/l-h-y168/DCBoost](https: //github. com/l-h-y168/DCBoost). **

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NeurIPS Conference 2024 Conference Paper

E-Motion: Future Motion Simulation via Event Sequence Diffusion

Song Wu
Zhiyu Zhu
Junhui Hou
Guangming Shi
Jinjian Wu

Forecasting a typical object's future motion is a critical task for interpreting and interacting with dynamic environments in computer vision. Event-based sensors, which could capture changes in the scene with exceptional temporal granularity, may potentially offer a unique opportunity to predict future motion with a level of detail and precision previously unachievable. Inspired by that, we propose to integrate the strong learning capacity of the video diffusion model with the rich motion information of an event camera as a motion simulation framework. Specifically, we initially employ pre-trained stable video diffusion models to adapt the event sequence dataset. This process facilitates the transfer of extensive knowledge from RGB videos to an event-centric domain. Moreover, we introduce an alignment mechanism that utilizes reinforcement learning techniques to enhance the reverse generation trajectory of the diffusion model, ensuring improved performance and accuracy. Through extensive testing and validation, we demonstrate the effectiveness of our method in various complex scenarios, showcasing its potential to revolutionize motion flow prediction in computer vision applications such as autonomous vehicle guidance, robotic navigation, and interactive media. Our findings suggest a promising direction for future research in enhancing the interpretative power and predictive accuracy of computer vision systems. The source code ispublicly available at https: //github. com/p4r4mount/E-Motion.

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NeurIPS Conference 2024 Conference Paper

Fine-grained Image-to-LiDAR Contrastive Distillation with Visual Foundation Models

Yifan Zhang
Junhui Hou

Contrastive image-to-LiDAR knowledge transfer, commonly used for learning 3D representations with synchronized images and point clouds, often faces a self-conflict dilemma. This issue arises as contrastive losses unintentionally dissociate features of unmatched points and pixels that share semantic labels, compromising the integrity of learned representations. To overcome this, we harness Visual Foundation Models (VFMs), which have revolutionized the acquisition of pixel-level semantics, to enhance 3D representation learning. Specifically, we utilize off-the-shelf VFMs to generate semantic labels for weakly-supervised pixel-to-point contrastive distillation. Additionally, we employ von Mises-Fisher distributions to structure the feature space, ensuring semantic embeddings within the same class remain consistent across varying inputs. Furthermore, we adapt sampling probabilities of points to address imbalances in spatial distribution and category frequency, promoting comprehensive and balanced learning. Extensive experiments demonstrate that our approach mitigates the challenges posed by traditional methods and consistently surpasses existing image-to-LiDAR contrastive distillation methods in downstream tasks. We have included the code in supplementary materials.

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NeurIPS Conference 2024 Conference Paper

Flatten Anything: Unsupervised Neural Surface Parameterization

Qijian Zhang
Junhui Hou
Wenping Wang
Ying He

Surface parameterization plays an essential role in numerous computer graphics and geometry processing applications. Traditional parameterization approaches are designed for high-quality meshes laboriously created by specialized 3D modelers, thus unable to meet the processing demand for the current explosion of ordinary 3D data. Moreover, their working mechanisms are typically restricted to certain simple topologies, thus relying on cumbersome manual efforts (e. g. , surface cutting, part segmentation) for pre-processing. In this paper, we introduce the Flatten Anything Model (FAM), an unsupervised neural architecture to achieve global free-boundary surface parameterization via learning point-wise mappings between 3D points on the target geometric surface and adaptively-deformed UV coordinates within the 2D parameter domain. To mimic the actual physical procedures, we ingeniously construct geometrically-interpretable sub-networks with specific functionalities of surface cutting, UV deforming, unwrapping, and wrapping, which are assembled into a bi-directional cycle mapping framework. Compared with previous methods, our FAM directly operates on discrete surface points without utilizing connectivity information, thus significantly reducing the strict requirements for mesh quality and even applicable to unstructured point cloud data. More importantly, our FAM is fully-automated without the need for pre-cutting and can deal with highly-complex topologies, since its learning process adaptively finds reasonable cutting seams and UV boundaries. Extensive experiments demonstrate the universality, superiority, and inspiring potential of our proposed neural surface parameterization paradigm. Our code is available at https: //github. com/keeganhk/FlattenAnything.

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NeurIPS Conference 2024 Conference Paper

PrefPaint: Aligning Image Inpainting Diffusion Model with Human Preference

Kendong Liu
Zhiyu Zhu
Chuanhao Li
Hui Liu
Huanqiang Zeng
Junhui Hou

In this paper, we make the first attempt to align diffusion models for image inpainting with human aesthetic standards via a reinforcement learning framework, significantly improving the quality and visual appeal of inpainted images. Specifically, instead of directly measuring the divergence with paired images, we train a reward model with the dataset we construct, consisting of nearly 51, 000 images annotated with human preferences. Then, we adopt a reinforcement learning process to fine-tune the distribution of a pre-trained diffusion model for image inpainting in the direction of higher reward. Moreover, we theoretically deduce the upper bound on the error of the reward model, which illustrates the potential confidence of reward estimation throughout the reinforcement alignment process, thereby facilitating accurate regularization. Extensive experiments on inpainting comparison and downstream tasks, such as image extension and 3D reconstruction, demonstrate the effectiveness of our approach, showing significant improvements in the alignment of inpainted images with human preference compared with state-of-the-art methods. This research not only advances the field of image inpainting but also provides a framework for incorporating human preference into the iterative refinement of generative models based on modeling reward accuracy, with broad implications for the design of visually driven AI applications. Our code and dataset are publicly available at \url{https: //prefpaint. github. io}.

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NeurIPS Conference 2023 Conference Paper

Global Structure-Aware Diffusion Process for Low-light Image Enhancement

Jinhui HOU
Zhiyu Zhu
Junhui Hou
Hui Liu
Huanqiang Zeng
Hui Yuan

This paper studies a diffusion-based framework to address the low-light image enhancement problem. To harness the capabilities of diffusion models, we delve into this intricate process and advocate for the regularization of its inherent ODE-trajectory. To be specific, inspired by the recent research that low curvature ODE-trajectory results in a stable and effective diffusion process, we formulate a curvature regularization term anchored in the intrinsic non-local structures of image data, i. e. , global structure-aware regularization, which gradually facilitates the preservation of complicated details and the augmentation of contrast during the diffusion process. This incorporation mitigates the adverse effects of noise and artifacts resulting from the diffusion process, leading to a more precise and flexible enhancement. To additionally promote learning in challenging regions, we introduce an uncertainty-guided regularization technique, which wisely relaxes constraints on the most extreme regions of the image. Experimental evaluations reveal that the proposed diffusion-based framework, complemented by rank-informed regularization, attains distinguished performance in low-light enhancement. The outcomes indicate substantial advancements in image quality, noise suppression, and contrast amplification in comparison with state-of-the-art methods. We believe this innovative approach will stimulate further exploration and advancement in low-light image processing, with potential implications for other applications of diffusion models. The code is publicly available at https: //github. com/jinnh/GSAD.

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NeurIPS Conference 2023 Conference Paper

NeuroGF: A Neural Representation for Fast Geodesic Distance and Path Queries

Qijian Zhang
Junhui Hou
Yohanes Adikusuma
Wenping Wang
Ying He

Geodesics play a critical role in many geometry processing applications. Traditional algorithms for computing geodesics on 3D mesh models are often inefficient and slow, which make them impractical for scenarios requiring extensive querying of arbitrary point-to-point geodesics. Recently, deep implicit functions have gained popularity for 3D geometry representation, yet there is still no research on neural implicit representation of geodesics. To bridge this gap, we make the first attempt to represent geodesics using implicit learning frameworks. Specifically, we propose neural geodesic field (NeuroGF), which can be learned to encode all-pairs geodesics of a given 3D mesh model, enabling to efficiently and accurately answer queries of arbitrary point-to-point geodesic distances and paths. Evaluations on common 3D object models and real-captured scene-level meshes demonstrate our exceptional performances in terms of representation accuracy and querying efficiency. Besides, NeuroGF also provides a convenient way of jointly encoding both 3D geometry and geodesics in a unified representation. Moreover, the working mode of per-model overfitting is further extended to generalizable learning frameworks that can work on various input formats such as unstructured point clouds, which also show satisfactory performances for unseen shapes and categories. Our code and data are available at https: //github. com/keeganhk/NeuroGF.

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AAAI Conference 2023 Conference Paper

PointCA: Evaluating the Robustness of 3D Point Cloud Completion Models against Adversarial Examples

Shengshan Hu
Junwei Zhang
Wei Liu
Junhui Hou
Minghui Li
Leo Yu Zhang
Hai Jin
Lichao Sun

Point cloud completion, as the upstream procedure of 3D recognition and segmentation, has become an essential part of many tasks such as navigation and scene understanding. While various point cloud completion models have demonstrated their powerful capabilities, their robustness against adversarial attacks, which have been proven to be fatally malicious towards deep neural networks, remains unknown. In addition, existing attack approaches towards point cloud classifiers cannot be applied to the completion models due to different output forms and attack purposes. In order to evaluate the robustness of the completion models, we propose PointCA, the first adversarial attack against 3D point cloud completion models. PointCA can generate adversarial point clouds that maintain high similarity with the original ones, while being completed as another object with totally different semantic information. Specifically, we minimize the representation discrepancy between the adversarial example and the target point set to jointly explore the adversarial point clouds in the geometry space and the feature space. Furthermore, to launch a stealthier attack, we innovatively employ the neighbourhood density information to tailor the perturbation constraint, leading to geometry-aware and distribution-adaptive modifications for each point. Extensive experiments against different premier point cloud completion networks show that PointCA can cause the performance degradation from 77.9% to 16.7%, with the structure chamfer distance kept below 0.01. We conclude that existing completion models are severely vulnerable to adversarial examples, and state-of-the-art defenses for point cloud classification will be partially invalid when applied to incomplete and uneven point cloud data.

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NeurIPS Conference 2023 Conference Paper

Unleash the Potential of Image Branch for Cross-modal 3D Object Detection

Yifan Zhang
Qijian Zhang
Junhui Hou
Yixuan Yuan
Guoliang Xing

To achieve reliable and precise scene understanding, autonomous vehicles typically incorporate multiple sensing modalities to capitalize on their complementary attributes. However, existing cross-modal 3D detectors do not fully utilize the image domain information to address the bottleneck issues of the LiDAR-based detectors. This paper presents a new cross-modal 3D object detector, namely UPIDet, which aims to unleash the potential of the image branch from two aspects. First, UPIDet introduces a new 2D auxiliary task called normalized local coordinate map estimation. This approach enables the learning of local spatial-aware features from the image modality to supplement sparse point clouds. Second, we discover that the representational capability of the point cloud backbone can be enhanced through the gradients backpropagated from the training objectives of the image branch, utilizing a succinct and effective point-to-pixel module. Extensive experiments and ablation studies validate the effectiveness of our method. Notably, we achieved the top rank in the highly competitive cyclist class of the KITTI benchmark at the time of submission. The source code is available at https: //github. com/Eaphan/UPIDet.

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NeurIPS Conference 2022 Conference Paper

Learning Graph-embedded Key-event Back-tracing for Object Tracking in Event Clouds

Zhiyu Zhu
Junhui Hou
Xianqiang Lyu

Event data-based object tracking is attracting attention increasingly. Unfortunately, the unusual data structure caused by the unique sensing mechanism poses great challenges in designing downstream algorithms. To tackle such challenges, existing methods usually re-organize raw event data (or event clouds) with the event frame/image representation to adapt to mature RGB data-based tracking paradigms, which compromises the high temporal resolution and sparse characteristics. By contrast, we advocate developing new designs/techniques tailored to the special data structure to realize object tracking. To this end, we make the first attempt to construct a new end-to-end learning-based paradigm that directly consumes event clouds. Specifically, to process a non-uniformly distributed large-scale event cloud efficiently, we propose a simple yet effective density-insensitive downsampling strategy to sample a subset called key-events. Then, we employ a graph-based network to embed the irregular spatio-temporal information of key-events into a high-dimensional feature space, and the resulting embeddings are utilized to predict their target likelihoods via semantic-driven Siamese-matching. Besides, we also propose motion-aware target likelihood prediction, which learns the motion flow to back-trace the potential initial positions of key-events and measures them with the previous proposal. Finally, we obtain the bounding box by adaptively fusing the two intermediate ones separately regressed from the weighted embeddings of key-events by the two types of predicted target likelihoods. Extensive experiments on both synthetic and real event datasets demonstrate the superiority of the proposed framework over state-of-the-art methods in terms of both the tracking accuracy and speed. The code is publicly available at https: //github. com/ZHU-Zhiyu/Event-tracking.

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AAAI Conference 2021 Conference Paper

Clustering Ensemble Meets Low-rank Tensor Approximation

Yuheng Jia
Hui Liu
Junhui Hou
Qingfu Zhang

This paper explores the problem of clustering ensemble, which aims to combine multiple base clusterings to produce better performance than that of the individual one. The existing clustering ensemble methods generally construct a coassociation matrix, which indicates the pairwise similarity between samples, as the weighted linear combination of the connective matrices from different base clusterings, and the resulting co-association matrix is then adopted as the input of an off-the-shelf clustering algorithm, e. g. , spectral clustering. However, the co-association matrix may be dominated by poor base clusterings, resulting in inferior performance. In this paper, we propose a novel low-rank tensor approximation based method to solve the problem from a global perspective. Specifically, by inspecting whether two samples are clustered to an identical cluster under different base clusterings, we derive a coherent-link matrix, which contains limited but highly reliable relationships between samples. We then stack the coherent-link matrix and the co-association matrix to form a three-dimensional tensor, the low-rankness property of which is further explored to propagate the information of the coherent-link matrix to the co-association matrix, producing a refined co-association matrix. We formulate the proposed method as a convex constrained optimization problem and solve it efficiently. Experimental results over 7 benchmark data sets show that the proposed model achieves a breakthrough in clustering performance, compared with 11 state-of-the-art methods. To the best of our knowledge, this is the first work to explore the potential of low-rank tensor on clustering ensemble, which is fundamentally different from previous approaches. Last but not least, our method only contains one parameter, which can be easily tuned.

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NeurIPS Conference 2020 Conference Paper

CoADNet: Collaborative Aggregation-and-Distribution Networks for Co-Salient Object Detection

Qijian Zhang
Runmin Cong
Junhui Hou
Chongyi Li
Yao Zhao

Co-Salient Object Detection (CoSOD) aims at discovering salient objects that repeatedly appear in a given query group containing two or more relevant images. One challenging issue is how to effectively capture co-saliency cues by modeling and exploiting inter-image relationships. In this paper, we present an end-to-end collaborative aggregation-and-distribution network (CoADNet) to capture both salient and repetitive visual patterns from multiple images. First, we integrate saliency priors into the backbone features to suppress the redundant background information through an online intra-saliency guidance structure. After that, we design a two-stage aggregate-and-distribute architecture to explore group-wise semantic interactions and produce the co-saliency features. In the first stage, we propose a group-attentional semantic aggregation module that models inter-image relationships to generate the group-wise semantic representations. In the second stage, we propose a gated group distribution module that adaptively distributes the learned group semantics to different individuals in a dynamic gating mechanism. Finally, we develop a group consistency preserving decoder tailored for the CoSOD task, which maintains group constraints during feature decoding to predict more consistent full-resolution co-saliency maps. The proposed CoADNet is evaluated on four prevailing CoSOD benchmark datasets, which demonstrates the remarkable performance improvement over ten state-of-the-art competitors.

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AAAI Conference 2020 Conference Paper

Learning Light Field Angular Super-Resolution via a Geometry-Aware Network

Jing Jin
Junhui Hou
Hui Yuan
Sam Kwong

The acquisition of light ﬁeld images with high angular resolution is costly. Although many methods have been proposed to improve the angular resolution of a sparsely-sampled light ﬁeld, they always focus on the light ﬁeld with a small baseline, which is captured by a consumer light ﬁeld camera. By making full use of the intrinsic geometry information of light ﬁelds, in this paper we propose an end-to-end learning-based approach aiming at angularly super-resolving a sparselysampled light ﬁeld with a large baseline. Our model consists of two learnable modules and a physically-based module. Speciﬁcally, it includes a depth estimation module for explicitly modeling the scene geometry, a physically-based warping for novel views synthesis, and a light ﬁeld blending module speciﬁcally designed for light ﬁeld reconstruction. Moreover, we introduce a novel loss function to promote the preservation of the light ﬁeld parallax structure. Experimental results over various light ﬁeld datasets including large baseline light ﬁeld images demonstrate the signiﬁcant superiority of our method when compared with state-of-the-art ones, i. e. , our method improves the PSNR of the second best method up to 2 dB in average, while saves the execution time 48×. In addition, our method preserves the light ﬁeld parallax structure better.

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JBHI Journal 2016 Journal Article

Facial Position and Expression-Based Human–Computer Interface for Persons With Tetraplegia

Zhen-Peng Bian
Junhui Hou
Lap-Pui Chau
Nadia Magnenat-Thalmann

A human-computer interface (namely Facial position and expression Mouse system, FM) for the persons with tetraplegia based on a monocular infrared depth camera is presented in this paper. The nose position along with the mouth status (close/open) is detected by the proposed algorithm to control and navigate the cursor as computer user input. The algorithm is based on an improved Randomized Decision Tree, which is capable of detecting the facial information efficiently and accurately. A more comfortable user experience is achieved by mapping the nose motion to the cursor motion via a nonlinear function. The infrared depth camera enables the system to be independent of illumination and color changes both from the background and on human face, which is a critical advantage over RGB camera-based options. Extensive experimental results show that the proposed system outperforms existing assistive technologies in terms of quantitative and qualitative assessments.

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JBHI Journal 2015 Journal Article

Fall Detection Based on Body Part Tracking Using a Depth Camera

Zhen-Peng Bian
Junhui Hou
Lap-Pui Chau
Nadia Magnenat-Thalmann

The elderly population is increasing rapidly all over the world. One major risk for elderly people is fall accidents, especially for those living alone. In this paper, we propose a robust fall detection approach by analyzing the tracked key joints of the human body using a single depth camera. Compared to the rivals that rely on the RGB inputs, the proposed scheme is independent of illumination of the lights and can work even in a dark room. In our scheme, a pose-invariant randomized decision tree algorithm is proposed for the key joint extraction, which requires low computational cost during the training and test. Then, the support vector machine classifier is employed to determine whether a fall motion occurs, whose input is the 3-D trajectory of the head joint. The experimental results demonstrate that the proposed fall detection method is more accurate and robust compared with the state-of-the-art methods.

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