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Yue Zhou

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13 papers

2 author rows

AAAI Conference 2026 Conference Paper

AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection

Bin-Bin Gao
Yue Zhou
Jiangtao Yan
Yuezhi Cai
Weixi Zhang
Meng Wang
Jun Liu
Yong Liu

Universal visual anomaly detection aims to identify anomalies from novel or unseen vision domains without additional fine-tuning, which is critical in open scenarios. Recent studies have demonstrated that pre-trained vision-language models like CLIP exhibit strong generalization with just zero or a few normal images. However, existing methods struggle to design prompt templates, handle complex token interactions, or require fine-tuning on target domains, resulting in limited flexibility. In this work, we present a simple yet effective AdaptCLIP based on two key insights. First, adaptive visual and textual representations should be learned alternately rather than jointly. Second, comparative learning between query and normal image prompt should incorporate both contextual and aligned residual features, rather than relying solely on residual features. AdaptCLIP treats CLIP models as a foundational service, adding only three simple adapters, visual adapter, textual adapter, and prompt-query adapter, at its input or output ends. AdaptCLIP supports zero-/few-shot generalization across domains and provides a training-free approach on target domains once trained on a base dataset. AdaptCLIP achieves state-of-the-art performance on 12 anomaly detection benchmarks from industrial and medical domains, significantly outperforming existing competitive methods.

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AAAI Conference 2026 Conference Paper

Hilbert Curve-Encoded Rotation-Equivariant Oriented Object Detector with Locality-Preserving Spatial Mapping

Qi Ming
Liuqian Wang
Juan Fang
Xudong Zhao
Yucheng Xu
Ziyi Teng
Yue Zhou
Xiaoxi Hu

Arbitrary-Oriented Object Detection (AOOD) has found broad applications in embodied intelligence, autonomous driving, and satellite remote sensing. However, current AOOD frameworks face challenges in ineffective feature extraction and orientation regression inaccuracy. Inspired by Hilbert curve's intrinsic locality-preserving property, we propose a flexible Hilbert curve-Encoded Rotation-Equivariant Oriented Object Detector (HERO-Det). Our innovations include: (i) a novel Hilbert curve traversal convolution paradigm with a dimensionality reduction scheme, which employs locality-preserving spatial filling curves for feature transformation, (ii) a Hilbert pyramid transformer enabling hierarchical construction of multi-scale feature sequences through space-folding operations, as well as (iii) an orientation-adaptive prediction head that decouples rotation-equivariant regression features from invariant classification cues to resolve orientation regression dilemmas in two-stage detectors. Extensive experiments show HERO-Det achieves state-of-the-art performance on AOOD benchmarks, with mAP of 79.56%, 90.64%, 90.10%, and 80.47% on DOTA, HRSC2016, SSDD, and HRSID, respectively. Performance gains in cross-task validation further demonstrate the versatility of our method to diverse vision tasks, such as medical image segmentation and 3D object detection.

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AAAI Conference 2026 Conference Paper

Where It Moves, It Matters: Referring Surgical Instrument Segmentation via Motion

Meng Wei
Kun Yuan
Shi Li
Yue Zhou
Long Bai
Nassir Navab
Hongliang Ren
Hong Joo Lee

Enabling intuitive, language-driven interaction with surgical scenes is a critical step toward intelligent operating rooms and autonomous surgical robotic assistance. However, the task of referring segmentation, localizing surgical instruments based on natural language descriptions, remains underexplored in surgical videos, with existing approaches struggling to generalize due to reliance on static visual cues and predefined instrument names. In this work, we introduce SurgRef, a novel motion-guided framework that grounds free-form language expressions in instrument motion, capturing how tools move and interact across time, rather than what they look like. This allows models to understand and segment instruments even under occlusion, ambiguity, or unfamiliar terminology. To train and evaluate SurgRef, we present Ref-IMotion, a diverse, multi-institutional video dataset with dense spatiotemporal masks and rich motion-centric expressions. SurgRef achieves state-of-the-art accuracy and generalization across surgical procedures, setting a new benchmark for robust, language-driven surgical video segmentation.

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NeurIPS Conference 2025 Conference Paper

Breaking Latent Prior Bias in Detectors for Generalizable AIGC Image Detection

Yue Zhou
Xinan He
Kaiqing Lin
Bing Fan
Feng Ding
Bin Li

Current AIGC detectors often achieve near-perfect accuracy on images produced by the same generator used for training but struggle to generalize to outputs from unseen generators. We trace this failure in part to latent prior bias: detectors learn shortcuts tied to patterns stemming from the initial noise vector rather than learning robust generative artifacts. To address this, we propose \textbf{On-Manifold Adversarial Training (OMAT)}: by optimizing the initial latent noise of diffusion models under fixed conditioning, we generate \emph{on-manifold} adversarial examples that remain on the generator’s output manifold—unlike pixel-space attacks, which introduce off-manifold perturbations that the generator itself cannot reproduce and that can obscure the true discriminative artifacts. To test against state-of-the-art generative models, we introduce GenImage++, a test-only benchmark of outputs from advanced generators (Flux. 1, SD3) with extended prompts and diverse styles. We apply our adversarial-training paradigm to ResNet50 and CLIP baselines and evaluate across existing AIGC forensic benchmarks and recent challenge datasets. Extensive experiments show that adversarially trained detectors significantly improve cross-generator performance without any network redesign. Our findings on latent-prior bias offer valuable insights for future dataset construction and detector evaluation, guiding the development of more robust and generalizable AIGC forensic methodologies.

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NeurIPS Conference 2025 Conference Paper

Guard Me If You Know Me: Protecting Specific Face-Identity from Deepfakes

Kaiqing Lin
Zhiyuan Yan
Ke-Yue Zhang
Li Hao
Yue Zhou
Yuzhen Lin
Weixiang Li
Taiping Yao

Securing personal identity against deepfake attacks is increasingly critical in the digital age, especially for celebrities and political figures whose faces are easily accessible and frequently targeted. Most existing deepfake detection methods focus on general-purpose scenarios and often ignore the valuable prior knowledge of known facial identities, e. g. , "VIP individuals" whose authentic facial data are already available. In this paper, we propose VIPGuard, a unified multimodal framework designed to capture fine-grained and comprehensive facial representations of a given identity, compare them against potentially fake or similar-looking faces, and reason over these comparisons to make accurate and explainable predictions. Specifically, our framework consists of three main stages. First, we fine-tune a multimodal large language model (MLLM) to learn detailed and structural facial attributes. Second, we perform identity-level discriminative learning to enable the model to distinguish subtle differences between highly similar faces, including real and fake variations. Finally, we introduce user-specific customization, where we model the unique characteristics of the target face identity and perform semantic reasoning via MLLM to enable personalized and explainable deepfake detection. Our framework shows clear advantages over previous detection works, where traditional detectors mainly rely on low-level visual cues and provide no human-understandable explanations, while other MLLM-based models often lack a detailed understanding of specific face identities. To facilitate the evaluation of our method, we build a comprehensive identity-aware benchmark called VIPBench for personalized deepfake detection, involving the latest 7 face-swapping and 7 entire face synthesis techniques for generation. Extensive experiments show that our model outperforms existing methods in both detection and explanation. The code is available at https: //github. com/KQL11/VIPGuard.

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NeurIPS Conference 2025 Conference Paper

VLForgery Face Triad: Detection, Localization and Attribution via Multimodal Large Language Models

Xinan He
Yue Zhou
Bing Fan
Bin Li
Guopu Zhu
Feng Ding

Faces synthesized by diffusion models (DMs) with high-quality and controllable attributes pose a significant challenge for Deepfake detection. Most state-of-the-art detectors only yield a binary decision, incapable of forgery localization, attribution of forgery methods, and providing analysis on the cause of forgeries. In this work, we integrate Multimodal Large Language Models (MLLMs) within DM-based face forensics, and propose a fine-grained analysis triad framework called VLForgery, that can 1) predict falsified facial images; 2) locate the falsified face regions subjected to partial synthesis; and 3) attribute the synthesis with specific generators. To achieve the above goals, we introduce VLF (Visual Language Forensics), a novel and diverse synthesis face dataset designed to facilitate rich interactions between Visual' and Language' modalities in MLLMs. Additionally, we propose an extrinsic knowledge-guided description method, termed EkCot, which leverages knowledge from the image generation pipeline to enable MLLMs to quickly capture image content. Furthermore, we introduce a low-level vision comparison pipeline designed to identify differential features between real and fake that MLLMs can inherently understand. These features are then incorporated into EkCot, enhancing its ability to analyze forgeries in a structured manner, following the sequence of detection, localization, and attribution. Extensive experiments demonstrate that VLForgery outperforms other state-of-the-art forensic approaches in detection accuracy, with additional potential for falsified region localization and attribution analysis.

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ICML Conference 2024 Conference Paper

CLIF: Complementary Leaky Integrate-and-Fire Neuron for Spiking Neural Networks

Yulong Huang
Xiaopeng Lin
Hongwei Ren
Haotian Fu
Yue Zhou
Zunchang Liu
Biao Pan
Bojun Cheng

Spiking neural networks (SNNs) are promising brain-inspired energy-efficient models. Compared to conventional deep Artificial Neural Networks (ANNs), SNNs exhibit superior efficiency and capability to process temporal information. However, it remains a challenge to train SNNs due to their undifferentiable spiking mechanism. The surrogate gradients method is commonly used to train SNNs, but often comes with an accuracy disadvantage over ANNs counterpart. We link the degraded accuracy to the vanishing of gradient on the temporal dimension through the analytical and experimental study of the training process of Leaky Integrate-and-Fire (LIF) Neuron-based SNNs. Moreover, we propose the Complementary Leaky Integrate-and-Fire (CLIF) Neuron. CLIF creates extra paths to facilitate the backpropagation in computing temporal gradient while keeping binary output. CLIF is hyperparameter-free and features broad applicability. Extensive experiments on a variety of datasets demonstrate CLIF’s clear performance advantage over other neuron models. Furthermore, the CLIF’s performance even slightly surpasses superior ANNs with identical network structure and training conditions. The code is available at https: //github. com/HuuYuLong/Complementary-LIF.

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ICLR Conference 2024 Conference Paper

SpikePoint: An Efficient Point-based Spiking Neural Network for Event Cameras Action Recognition

Hongwei Ren
Yue Zhou
Xiaopeng Lin
Yulong Huang
Haotian Fu
Jie Song
Bojun Cheng

Event cameras are bio-inspired sensors that respond to local changes in light intensity and feature low latency, high energy efficiency, and high dynamic range. Meanwhile, Spiking Neural Networks (SNNs) have gained significant attention due to their remarkable efficiency and fault tolerance. By synergistically harnessing the energy efficiency inherent in event cameras and the spike-based processing capabilities of SNNs, their integration could enable ultra-low-power application scenarios, such as action recognition tasks. However, existing approaches often entail converting asynchronous events into conventional frames, leading to additional data mapping efforts and a loss of sparsity, contradicting the design concept of SNNs and event cameras. To address this challenge, we propose SpikePoint, a novel end-to-end point-based SNN architecture. SpikePoint excels at processing sparse event cloud data, effectively extracting both global and local features through a singular-stage structure. Leveraging the surrogate training method, SpikePoint achieves high accuracy with few parameters and maintains low power consumption, specifically employing the identity mapping feature extractor on diverse datasets. SpikePoint achieves state-of-the-art (SOTA) performance on four event-based action recognition datasets using only 16 timesteps, surpassing other SNN methods. Moreover, it also achieves SOTA performance across all methods on three datasets, utilizing approximately 0.3 % of the parameters and 0.5 % of power consumption employed by artificial neural networks (ANNs). These results emphasize the significance of Point Cloud and pave the way for many ultra-low-power event-based data processing applications.

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AAAI Conference 2023 Conference Paper

GradPU: Positive-Unlabeled Learning via Gradient Penalty and Positive Upweighting

Songmin Dai
Xiaoqiang Li
Yue Zhou
Xichen Ye
Tong Liu

Positive-unlabeled learning is an essential problem in many real-world applications with only labeled positive and unlabeled data, especially when the negative samples are difficult to identify. Most existing positive-unlabeled learning methods will inevitably overfit the positive class to some extent due to the existence of unidentified positive samples. This paper first analyzes the overfitting problem and proposes to bound the generalization errors via Wasserstein distances. Based on that, we develop a simple yet effective positive-unlabeled learning method, GradPU, which consists of two key ingredients: A gradient-based regularizer that penalizes the gradient norms in the interpolated data region, which improves the generalization of positive class; An unnormalized upweighting mechanism that assigns larger weights to those positive samples that are hard, not-well-fitted and less frequently labeled. It enforces the training error of each positive sample to be small and increases the robustness to the labeling bias. We evaluate our proposed GradPU on three datasets: MNIST, FashionMNIST, and CIFAR10. The results demonstrate that GradPU achieves state-of-the-art performance on both unbiased and biased positive labeling scenarios.

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NeurIPS Conference 2023 Conference Paper

H2RBox-v2: Incorporating Symmetry for Boosting Horizontal Box Supervised Oriented Object Detection

Yi Yu
Xue Yang
Qingyun Li
Yue Zhou
Feipeng Da
Junchi Yan

With the rapidly increasing demand for oriented object detection, e. g. in autonomous driving and remote sensing, the recently proposed paradigm involving weakly-supervised detector H2RBox for learning rotated box (RBox) from the more readily-available horizontal box (HBox) has shown promise. This paper presents H2RBox-v2, to further bridge the gap between HBox-supervised and RBox-supervised oriented object detection. Specifically, we propose to leverage the reflection symmetry via flip and rotate consistencies, using a weakly-supervised network branch similar to H2RBox, together with a novel self-supervised branch that learns orientations from the symmetry inherent in visual objects. The detector is further stabilized and enhanced by practical techniques to cope with peripheral issues e. g. angular periodicity. To our best knowledge, H2RBox-v2 is the first symmetry-aware self-supervised paradigm for oriented object detection. In particular, our method shows less susceptibility to low-quality annotation and insufficient training data compared to H2RBox. Specifically, H2RBox-v2 achieves very close performance to a rotation annotation trained counterpart -- Rotated FCOS: 1) DOTA-v1. 0/1. 5/2. 0: 72. 31%/64. 76%/50. 33% vs. 72. 44%/64. 53%/51. 77%; 2) HRSC: 89. 66% vs. 88. 99%; 3) FAIR1M: 42. 27% vs. 41. 25%.

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JBHI Journal 2022 Journal Article

Cross-Model Attention-Guided Tumor Segmentation for 3D Automated Breast Ultrasound (ABUS) Images

Yue Zhou
Houjin Chen
Yanfeng Li
Xuyang Cao
Shu Wang
Dinggang Shen

Tumor segmentation in 3D automated breast ultrasound (ABUS) plays an important role in breast disease diagnosis and surgical planning. However, automatic segmentation of tumors in 3D ABUS images is still challenging, due to the large tumor shape and size variations, and uncertain tumor locations among patients. In this paper, we develop a novel cross-model attention-guided tumor segmentation network with a hybrid loss for 3D ABUS images. Specifically, we incorporate the tumor location into a segmentation network by combining an improved 3D Mask R-CNN head into V-Net as an end-to-end architecture. Furthermore, we introduce a cross-model attention mechanism that is able to aggregate the segmentation probability map from the improved 3D Mask R-CNN to each feature extraction level in the V-Net. Then, we design a hybrid loss to balance the contribution of each part in the proposed cross-model segmentation network. We conduct extensive experiments on 170 3D ABUS from 107 patients. Experimental results show that our method outperforms other state-of-the-art methods, by achieving the Dice similarity coefficient (DSC) of 64. 57%, Jaccard coefficient (JC) of 53. 39%, recall (REC) of 64. 43%, precision (PRE) of 74. 51%, 95th Hausdorff distance (95HD) of 11. 91 mm, and average surface distance (ASD) of 4. 63 mm. Our code will be available online ( https://github.com/zhouyuegithub/CMVNet ).

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TIST Journal 2011 Journal Article

Recognizing pair-activities by causality analysis

Yue Zhou
Bingbing Ni
Shuicheng Yan
Thomas S. Huang

In this article, beyond solo-activity analysis for single object, we study the more complicated pair-activity recognition problem by exploring the relationship between two active objects based on their trajectory clues obtained from video sensor. Our contributions are three-fold. First, we design two sets of features for representing the pair-activities encoded as length-variable trajectory pairs. One set characterizes the strength of causality between two trajectories, for example, the causality ratio and feedback ratio based on the Granger Causality Test (GCT), and another set describes the style of causality between two trajectories, for example, the sampled frequency responses of the digital filter with these two trajectories as the input and output discrete signals respectively. These features along with conventional velocity and position features of a trajectory-pair are essentially of multi-modalities, and may be greatly different in scales and importance. To make full use of them, we then develop a novel feature fusing procedure to learn the coefficients for weighting these features by maximizing the discriminating power measured by weighted correlation. Finally, we collected a pair-activity database of five popular categories, each of which consists of about 170 instances. The extensive experiments on this database validate the effectiveness of the designed features for pair-activity representation, and also demonstrate that the proposed feature fusing procedure significantly boosts the pair-activity classification accuracy.

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EAAI Journal 2011 Journal Article

Robust facial feature points extraction in color images

Yue Zhou
Yin Li
Zheng Wu
Meilin Ge

A method of facial feature points extraction based on improved active appearance model (AAM) with Gabor wavelet features was presented in the paper. After the pre-processing of a standard face detector and lighting compensation, the paper proposed a hybrid AAM by combining the local skin similarity with the original local grey-level appearance model. Moreover, the feature points by the hybrid AAM and their neighbors were considered by a classification problem to further refine the results. Namely, the Gabor feature around the feature points was extracted, trained by linear discriminant analysis (LDA) and classified by K Nearest Neighbor (KNN) to give the precise location of the feature points. Experimental results indicated that facial feature points can be located robustly and precisely by the proposed method.

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