GenAI4ELab/papercli-papers huggingface dataset:
S²Teacher: Step-by-step Teacher for Sparsely Annotated Oriented Object Detection
Authors: Yu Lin; Jianghang Lin; Kai Ye; You Shen; Shengchuan Zhang; Liujuan Cao
Venue: AAAI 2026 (main)

Abstract: Although fully-supervised oriented object detection has made significant progress in remote sensing image understanding, it comes at the cost of labor-intensive annotation. Recent studies have explored weakly and semi-supervised learning to alleviate this burden. However, these methods overlook the difficulties posed by dense annotations in complex remote sensing scenes. In this paper, we introduce a novel setting called sparsely annotated oriented object detection (SAOOD), which only labels partial instances, and propose a solution to address its challenges. Specifically, we focus on two key issues in the setting: (1) sparse labeling leading to overfitting on limited foreground representations, and (2) unlabeled objects (false negatives) confusing feature learning. To this end, we propose the S2Teacher, a novel angle-consistency guided method that progressively mines pseudo-labels for unlabeled objects from easy to hard, enhancing foreground representations. Additionally, it reweights the loss of unlabeled objects to mitigate their impact during training. Extensive experiments demonstrate that S2Teacher not only significantly improves detector performance across different sparse annotation levels but also achieves near-fully-supervised performance on the DOTA dataset with only 10% annotation instances, effectively balancing accuracy and labeling cost.
Frequency-Aligned Cross-Modal Learning with Top-K Wavelet Fusion and Dynamic Expert Routing for Enhanced Retinal Disease Diagnosis
Authors: Yuxin Lin; Haoran Li; Haoyu Cao; Yongting Hu; Qihao Xu; Chengliang Liu; Xiaoling Luo; Zhihao Wu; Yong Xu; Wei Wang
Venue: AAAI 2026 (main)

Abstract: Multimodal fusion of color fundus photography (CFP) and optical coherence tomography (OCT) B-scan images has demonstrated superior diagnostic potential for retinal diseases compared to single-modality approaches. However, existing fusion paradigms - whether through naive concatenation or attention mechanisms - treat cross-modal interactions indiscriminately, lacking adaptive modulation of modality-specific contributions under varying clinical scenarios. We propose an adaptive fusion framework that dynamically routes and refines multimodal signals for enhancing disease recognition. The framework comprises two key components: 1) Dynamic Cross-Modal Expert Routing (CMER), which selectively activates convolutional neural network (CNN) experts from one modality based on contextual guidance from the other, ensuring only the most relevant feature extractors contribute to fusion; and 2) Top-K Expert-Guided Wavelet Fusion (TEWF), which performs discrete wavelet transform (DWT) to decompose selected features into low- and high-frequency subbands. Cross-modal attention is then applied specifically to high-frequency components, where lesion-specific microstructures reside, enabling frequency-aware fusion. Finally, inverse DWT (IDWT) reconstructs the fused representation, weighted by CMER-derived importance scores to amplify informative modality cues while suppressing redundancy. Experimental validation on two multimodal retinal datasets demonstrates that our method achieves state-of-the-art performance, outperforming existing fusion strategies by significant margins in disease classification accuracy and robustness.
Commonality in Few: Few-Shot Multimodal Anomaly Detection via Hypergraph-Enhanced Memory
Authors: Yuxuan Lin; Hanjing Yan; Xuan Tong; Yang Chang; Huanzhen Wang; Ziheng Zhou; Shuyong Gao; Yan Wang; Wenqiang Zhang
Venue: AAAI 2026 (main)

Abstract: Few-shot multimodal industrial anomaly detection is a critical yet underexplored task, offering the ability to quickly adapt to complex industrial scenarios. In few-shot settings, insufficient training samples often fail to cover the diverse patterns present in test samples. This challenge can be mitigated by extracting structural commonality from a small number of training samples. In this paper, we propose a novel few-shot unsupervised multimodal industrial anomaly detection method based on structural commonality, CIF (Commonality In Few). To extract intra-class structural information, we employ hypergraphs, which are capable of modeling higher-order correlations, to capture the structural commonality within training samples, and use a memory bank to store this intra-class structural prior. Firstly, we design a semantic-aware hypergraph construction module tailored for single-semantic industrial images, from which we extract common structures to guide the construction of the memory bank. Secondly, we use a training-free hypergraph message passing module to update the visual features of test samples, reducing the distribution gap between test features and features in the memory bank. We further propose a hyperedge-guided memory search module, which utilizes structural information to assist the memory search process and reduce the false positive rate. Experimental results on the MVTec 3D-AD dataset and the Eyecandies dataset show that our method outperforms the state-of-the-art (SOTA) methods in few-shot settings.
CrossCut: Cross-Patch Aware Interactive Segmentation for Remote Sensing Images
Authors: Zheng Lin; Nan Zhou; Yuhan Wang; Bojian Zhang
Venue: AAAI 2026 (main)

Abstract: Interactive segmentation aims to delineate a user-specified target in an image by leveraging positive and negative clicks. While effective on natural images, existing methods often fail in remote sensing scenarios, where satellite imagery is characterized by ultra-high resolution, sparse object distribution, and significant scale variation. These factors hinder accurate segmentation of fine-grained targets like roads, buildings, and aircraft. To overcome these problems, we propose CrossCut, a novel interactive segmentation framework tailored for remote sensing imagery. Unlike previous approaches that either process the entire image or treat each patch independently, CrossCut enables simultaneous segmentation across multiple patches by propagating user click information to all patches. This design allows the model to fully utilize click guidance regardless of object location, effectively resolving the challenge of inter-patch information isolation. Furthermore, CrossCut supports flexible inference by allowing segmentation results from different patch configurations to be fused, enhancing both accuracy and robustness. Extensive evaluations across multiple remote sensing datasets demonstrate that CrossCut achieves state-of-the-art performance. Quantitative results and visualizations show that CrossCut significantly advances the field of interactive segmentation for remote sensing imagery.
MoFu: Scale-Aware Modulation and Fourier Fusion for Multi-Subject Video Generation
Authors: Run Ling; Ke Cao; Jian Lu; Ao Ma; Haowei Liu; Runze He; Changwei Wang; Rongtao Xu; Yihua Shao; Zhanjie Zhang; Peng Wu; Guibing Guo; Wei Feng; Zheng Zhang; Jingjing Lv; Junjie Shen; Ching Law; Xingwei Wang
Venue: AAAI 2026 (main)

Abstract: Multi-subject video generation aims to synthesize videos from textual prompts and multiple reference images, ensuring that each subject preserves natural scale and visual fidelity. However, current methods face two challenges: scale inconsistency, where variations in subject size lead to unnatural generation, and permutation sensitivity, where the order of reference inputs causes subject distortion.
In this paper, we propose MoFu, a unified framework that tackles both challenges. For scale inconsistency, we introduce Scale-Aware Modulation (SMO), an LLM-guided module that extracts implicit scale cues from the prompt and modulates features to ensure consistent subject sizes. To address permutation sensitivity, we present a simple yet effective Fourier Fusion strategy that processes the frequency information of reference features via the Fast Fourier Transform to produce a unified representation. Besides, we design a Scale-Permutation Stability Loss to jointly encourage scale-consistent and permutation-invariant generation.
To further evaluate these challenges, we establish a dedicated benchmark with controlled variations in subject scale and reference permutation. Extensive experiments demonstrate that MoFu significantly outperforms existing methods in preserving natural scale, subject fidelity, and overall visual quality.
CIA: Cluster-Instance Alignment for Unsupervised Day-Night Vehicle Re-Identification
Authors: Yongguo Ling; Chen Zhang; Yiming Liu; Wenhao Shao
Venue: AAAI 2026 (main)

Abstract: Cross-time vehicle re-identification (Re-ID), especially across day and night conditions, remains a challenging problem due to drastic illumination variations that lead to significant domain shifts. While existing methods perform well under daytime scenarios, their effectiveness degrades severely in cross-domain settings, and fully supervised solutions demand costly annotations in both domains. In this paper, we introduce a new setting, Unsupervised Day-Night Vehicle Re-Identification (USL-DN-ReID), and propose a novel Cluster-Instance Alignment (CIA) framework to address it. CIA performs dual-level alignment: 1) at the cluster level, a Dictionary-Guided Graph Matching (DGM) module builds a cross-domain topological graph using soft similarities among cluster centers and solves global matching via the Hungarian algorithm; 2) at the instance level, a Multi-Factor Adaptive Alignment (MAA) module introduces a multi-factor adaptive weighting strategy that emphasizes high-confidence pairwise relations while suppressing noise. Together, these components enable robust and scalable cross-domain adaptation without requiring target-domain labels. Extensive experiments conducted on the DN-348 and DN-Wild benchmarks demonstrate the effectiveness and superiority of the proposed CIA framework, setting new state-of-the-art results on both datasets.
DenoiseGS: Delta-Based 3D Gaussian Splatting with B-spline Trajectory Optimization for Dynamic Driving Scene Reconstruction
Authors: Junjie Linghu; Qiang Ling
Venue: AAAI 2026 (main)

Abstract: 3D street scene reconstruction is a challenging yet crucial task for autonomous driving. Many reconstruction methods often overlook two key limitations for high-quality driving scene reconstruction, sensitivity to camera parameter noise from high-speed vehicles and heavy reliance on precise dynamic object annotations of datasets. To resolve these issues, we propose DenoiseGS, a simple yet effective approach based on explicit 3D Gaussian splatting. Specifically, we propose a novel learnable Delta attribute per Gaussian primitive that operates on the image plane during rasterization to mitigate the impact of noisy camera parameters through modulating the inputs of the alpha-blending process. To enhance the representation of this Delta attribute, we propose a DeltaEstimator that encodes viewing direction and contextual cues to facilitate view dependence. We also extend additional CUDA operations to enable efficient gradient update for the delta attribute. Furthermore, to overcome the limitation of inaccurate annotations for dynamic objects, we propose a learnable B-spline trajectory optimization with a few control points to model the trajectory of a moving object. Comprehensive experiments conducted on nuScenes and Waymo Open Dataset demonstrate that our DenoiseGS outperforms some state-of-the-art methods across all metrics of both reconstruction quality and novel view synthesis.
Pb4U-GNet: Resolution-Adaptive Garment Simulation via Propagation-before-Update Graph Network
Authors: Aoran Liu; Kun Hu; Clinton Ansun Mo; Qiuxia Wu; Wenxiong Kang; Zhiyong Wang
Venue: AAAI 2026 (main)

Abstract: Garment simulation is fundamental to various applications in computer vision and graphics, from virtual try-on to digital human modelling. However, conventional physics-based methods remain computationally expensive, hindering their application in time-sensitive scenarios. While graph neural networks (GNNs) offer promising acceleration, existing approaches exhibit poor cross-resolution generalisation, demonstrating significant performance degradation on higher-resolution meshes beyond the training distribution. This stems from two key factors: (1) existing GNNs employ fixed message-passing depth that fails to adapt information aggregation to mesh density variation, and (2) vertex-wise displacement magnitudes are inherently resolution-dependent in garment simulation.
To address these issues, we introduce Propagation-before-Update Graph Network (Pb4U-GNet), a resolution-adaptive framework that decouples message propagation from feature updates. Pb4U-GNet incorporates two key mechanisms: (1) dynamic propagation depth control, adjusting message-passing iterations based on mesh resolution, and (2) geometry-aware update scaling, which scales predictions according to local mesh characteristics. Extensive experiments show that even trained solely on low-resolution meshes, Pb4U-GNet exhibits strong generalisability across diverse mesh resolutions, addressing a fundamental challenge in neural garment simulation.
CausalCLIP: Causally-Informed Feature Disentanglement and Filtering for Generalizable Detection of Generated Images
Authors: Bo Liu; Qiao Qin; Qinghui He
Venue: AAAI 2026 (main)

Abstract: The rapid advancement of generative models has increased the demand for generated image detectors capable of generalizing across diverse and evolving generation techniques. However, existing methods, including those leveraging pre-trained vision-language models, often produce highly entangled representations, mixing task-relevant forensic cues (causal features) with spurious or irrelevant patterns (non-causal features), thus limiting generalization. To address this issue, we propose CausalCLIP, a framework that explicitly disentangles causal from non-causal features and employs targeted filtering guided by causal inference principles to retain only the most transferable and discriminative forensic cues. By modeling the generation process with a structural causal model and enforcing statistical independence through Gumbel-Softmax-based feature masking and Hilbert-Schmidt Independence Criterion (HSIC) constraints, CausalCLIP isolates stable causal features robust to distribution shifts. When tested on unseen generative models from different series, CausalCLIP demonstrates strong generalization ability, achieving improvements of 6.83% in accuracy and 4.06% in average precision over state-of-the-art methods.
RCP-LO: A Relative Coordinate Prediction Framework for Generalizable Deep LiDAR Odometry
Authors: Chen Liu; Wen Li; Yongshu Huang; Minghang Zhu; Yuyang Yang; Dunqiang Liu; Sheng Ao; Cheng Wang
Venue: AAAI 2026 (main)

Abstract: LiDAR odometry is a critical component of SLAM in autonomous driving and robotics. Learning-based methods have shown remarkable performance by regressing relative poses in an end-to-end manner. However, when applying these trained models, originally developed on the widely used KITTI dataset, to other scenes, performance often drops significantly. In other words, existing methods struggle to generalize well to new environments. To address this challenge, we propose RCP-LO, a simple yet effective LiDAR odometry framework. 
We introduce a novel representation for relative poses, reformulating them as relative coordinates, which can then be solved using geometrical verification. This approach avoids overly simplified pose representations and makes better use of scene geometry, thereby improving generalization.
Moreover, to capture the inherent uncertainties in relative pose estimation from occluded LiDAR point clouds from dynamic environments, we adapt our framework to learn a denoising diffusion model, allowing for sampling plausible relative coordinates while improving robustness. We also introduce a differentiable geometric weighted singular value decomposition module, enabling efficient pose estimation through a single forward pass. 
Extensive experiments demonstrate that RCP-LO, trained exclusively on the KITTI dataset, achieves competitive performance compared to SOTA learning-based methods and generalizes effectively to the KITTI-360, Ford, and Oxford datasets.
Rethinking Bias in Generative Data Augmentation for Medical AI: A Frequency Recalibration Method
Authors: Chi Liu; Jincheng Liu; Congcong Zhu; Minghao Wang; Sheng Shen; Jia Gu; Tianqing Zhu; Wanlei Zhou
Venue: AAAI 2026 (main)

Abstract: Developing Medical AI relies on large datasets and easily suffers from data scarcity. Generative data augmentation (GDA) using AI generative models offers a solution to synthesize realistic medical images. However, the bias in GDA is often underestimated in medical domains, with concerns about the risk of introducing detrimental features generated by AI and harming downstream tasks. This paper identifies the frequency misalignment between real and synthesized images as one of the key factors underlying unreliable GDA and proposes the Frequency Recalibration (FreRec) method to reduce the frequency distributional discrepancy and thus improve GDA. FreRec involves (1) Statistical High-frequency Replacement (SHR) to roughly align high-frequency components and (2) Reconstructive High-frequency Mapping (RHM) to enhance image quality and reconstruct high-frequency details. Extensive experiments were conducted in various medical datasets, including brain MRIs, chest X-rays, and fundus images. The results show that FreRec significantly improves downstream medical image classification performance compared to uncalibrated AI-synthesized samples. FreRec is a standalone post-processing step that is compatible with any generative model and can integrate seamlessly with common medical GDA pipelines.
De-biased Natural Language Egocentric Task Verification via Prototypical Evidence Learning
Authors: Chong Liu; Xun Jiang; Fumin Shen; Lei Zhu; Jingkuan Song; Heng Tao Shen; Xing Xu
Venue: AAAI 2026 (main)

Abstract: Natural Language-based Egocentric Task Verification (NLETV) aims to verify the alignment between action sequences in egocentric videos and their corresponding textual descriptions. 
However, existing NLETV approaches are still facing two critical challenges: 
(1) These methods are designed for simulating environments, ignoring the domain gap between synthetic and realistic data. 
(2) The matching processes are regarded as a simple binary classification problem, which undermines model reliability due to evaluation bias and uncalibrated decision settings. 
To address these challenges, we propose a novel method termed Prototypical Evidential Learning (PEL), which can be adapted to existing NLETV approaches and boost the model generalization and mitigate prediction bias. 
Our method leverages prototypes to guide cross-domain alignment and evidence collection. 
Specifically, PEL consists of two key components: 
(1) Prototypical Domain Adaptation module enabling cross-domain feature alignment and intra-domain prototype preservation between synthetic and realistic domains; 
(2) Matching Evidence Collector module, which quantifies prediction uncertainty on the prototypical representations through evidential deep learning. 
It enforces the model to collect the vision-text consistency and discrepancy evidence, thus addressing the issues of biased decisions in binary classification. 
Extensive experiments on two public datasets demonstrate that our PEL method outperforms existing state-of-the-art NLETV methods and shows remarkable generalizability.
Learning to Cluster Rare Cell Types: Implicit Semantic Data Augmentation for Spatial Multi-modal Omics Analysis
Authors: Daixian Liu; Hau-Sing So; Haoran Chen; Jiao Li; Shanshan Wang; Mengzhu Wang; Jingcai Guo
Venue: AAAI 2026 (main)

Abstract: Spatial multi-modal omics technologies have transformed biological research by enabling the simultaneous profiling of gene expression, protein abundance, and chromatin accessibility within their native spatial contexts. Despite these advances, accurately clustering rare cell types remains a major challenge due to data sparsity, high dimensionality, and limited annotated samples. While Graph Neural Networks (GNNs) have shown potential in modeling spatial omics data, their effectiveness is often constrained by the use of fixed K-nearest neighbor (KNN) graph structures, which fail to capture latent semantic relationships masked by sequencing noise. To overcome these limitations, we propose CRCT (Clustering Rare Cell Types): a novel framework that combines Implicit Semantic Data Augmentation (ISDA) with adaptive graph learning for spatial multi-modal omics analysis. Unlike traditional augmentation strategies that generate explicit synthetic samples, CRCT operates in the deep feature space by dynamically estimating intra-class covariance matrices and implicitly perturbing features along semantically meaningful directions. This enables effective augmentation for rare cell populations while preserving biological fidelity. Extensive experiments across four real-world datasets (HLN, MB, Stereo‑CITE‑seq, and SPOTS) and one synthetic benchmark demonstrate the state-of-the-art performance of CRCT, achieving improvements of up to +1.7 NMI and +7.8 ARI over strong baseline methods.
Spatial-Spectral Homogeneous Attacks on Physical-World Large Vision-Language Models
Authors: Daizong Liu; Baoquan Chen; Wei Hu
Venue: AAAI 2026 (main)

Abstract: Although large vision-language models (LVLMs) have demonstrated promising versatile capabilities on various downstream tasks, they are shown to be susceptible to adversarial examples. Existing LVLM attackers simply implement adversarial patterns in an impracticable setting: i) add digital global perturbations to entire input image; ii) access prior knowledge of LVLMs for optimization; iii) do not consider realistic transformations. These make them difficult to deploy in the physical-world attack scenarios. Motivated by the research gap and counter-practice phenomenon, this paper proposes the first practical LVLM attack method based on a novel adversarial patch design, which can achieve physical and digital attack settings without using any LVLM details. In particular, we introduce adversarial homogeneous constraints in both spatial and spectral domains to improve the patch stealthy for resisting potential real-world defenses. Besides, we also develop a new technique for synthesizing reasonably realistic transformations that capture the expected patch appearance variations in daily life. Extensive experiments are conducted to verify the strong adversarial capabilities of our proposed attack against prevalent LVLMs spanning a spectrum of tasks.