GenAI4ELab/papercli-papers huggingface dataset:
Self-calibration Enhanced Whole Slide Pathology Image Analysis
Authors: Haoming Luo; Xiaotian Yu; Shengxuming Zhang; Jiabin Xia; Jian Yang; Yuning Sun; Xiuming Zhang; Jing Zhang; Zunlei Feng
Venue: IJCAI 2025 (main)

Abstract: Pathology images are considered the ``gold standard" for cancer diagnosis and treatment, with gigapixel images providing extensive tissue and cellular information. Existing methods fail to simultaneously extract global structural and local detail features for comprehensive pathology image analysis efficiently. To address these limitations, we propose a self-calibration enhanced framework for whole slide pathology image analysis, comprising three components: a global branch, a focus predictor, and a detailed branch. The global branch initially classifies using the pathological thumbnail, while the focus predictor identifies relevant regions for classification based on the last layer features of the global branch. The detailed extraction branch then assesses whether the magnified regions correspond to the lesion area. Finally, a feature consistency constraint between the global and detail branches ensures that the global branch focuses on the appropriate region and extracts sufficient discriminative features for final identification. These focused discriminative features can facilitate the discovery of novel prognostic tumor markers, from the perspective of feature uniqueness and tissue spatial distribution. Extensive experiment results demonstrate that the proposed framework can rapidly deliver accurate and explainable results for pathological grading and prognosis tasks.
Noise Optimized Conditional Diffusion for Domain Adaptation
Authors: Lingkun Luo; Shiqiang Hu; Liming Chen
Venue: IJCAI 2025 (main)

Abstract: Pseudo-labeling is a cornerstone of Unsupervised Domain Adaptation (UDA), yet the scarcity of High-Confidence Pseudo-Labeled Target Domain Samples (hcpl-tds) often leads to inaccurate cross-domain statistical alignment, causing DA failures. To address this challenge, we propose Noise Optimized Conditional Diffusion for Domain Adaptation (NOCDDA), which seamlessly integrates the generative capabilities of conditional diffusion models with the decision-making requirements of DA to achieve task-coupled optimization for efficient adaptation. For robust cross-domain consistency, we modify the DA classifier to align with the conditional diffusion classifier within a unified optimization framework, enabling forward training on noise-varying cross-domain samples. Furthermore, we argue that the conventional N(0,I) initialization in diffusion models often generates class-confused hcpl-tds, compromising discriminative DA. To resolve this, we introduce a class-aware noise optimization strategy that refines sampling regions for reverse class-specific hcpl-tds generation, effectively enhancing cross-domain alignment. Extensive experiments across 5 benchmark datasets and 29 DA tasks demonstrate significant performance gains of NOCDDA over 31 state-of-the-art methods, validating its robustness and effectiveness.
SE(3)-Equivariant Diffusion Models for 3D Object Analysis
Authors: Xie Min; Zhao Jieyu; Shen Kedi; Chen Kangxin
Venue: IJCAI 2025 (main)

Abstract: SE(3)-equivariance is a critical property for capturing pose information in 3D vision tasks, enabling models to handle transformations such as rotations and translations effectively. While equivariant diffusion models have recently demonstrated promise in 3D object reassembly due to their generative and denoising capabilities, they face key challenges when applied to this task. Specifically, traditional diffusion models rely on fixed input sizes, which limits their adaptability to varying part quantities, and their linear noise addition and removal processes struggle to address the inherently nonlinear transformations of 3D parts. To overcome these limitations, this paper proposes an SE(3)-equivariant diffusion model for pose denoising and 3D object reassembly from fragmented parts. The model incorporates an equivariant encoder to extract SE(3)-equivariant features, a Lie algebra mapping to linearize noise addition and removal, and an elastic diffusion framework capable of adapting to varying part quantities and nonlinear transformations. By leveraging these components, the method achieves accurate and robust pose predictions across diverse input configurations. Experiments conducted on the Breaking Bad dataset, a real-world RePAIR and a self-constructed 3D mannequin dataset demonstrate the effectiveness of the proposed model, outperforming state-of-the-art methods across metrics such as root mean square error and part accuracy. Ablation studies further validate the critical contributions of key modules, emphasizing their roles in improving accuracy and robustness for 3D part reassembly tasks.
Towards Robust Deterministic and Probabilistic Modeling for Predictive Learning
Authors: Xuesong Nie; Haoyuan Jin; Vijayakumar Bhagavatula; Xiaofeng Liu
Venue: IJCAI 2025 (main)

Abstract: Predictive modeling of unannotated spatiotemporal data presents inherent challenges, primarily due to the highly entangled visual dynamics in real-world scenes. To tackle these complexities, we introduce a novel insight through Disentangling Deterministic and Probabilistic (DDP) modeling. We note a key observation in spatiotemporal data where low-level details typically remain stable, whereas high-level motion frequently exhibits dynamic variations. The core motivation involves constructing two distinct pathways in the latent space: a deterministic path and a probabilistic path. The probabilistic path begins by defining the motion flow, which explicitly describes complex many-to-many motion patterns between patches, and models its probabilistic distribution using a motion diffuser. The deterministic path incorporates a spectral-aware enhancer to retain and amplify visual details in the frequency domain. These designs ensure visual consistency while also capturing intricate long-term motion dynamics. Extensive experiments demonstrate the superiority of DDP across diverse scenario evaluations.
Stabilizing Holistic Semantics in Diffusion Bridge for Image Inpainting
Authors: Jinjia Peng; Mengkai Li; Huibing Wang
Venue: IJCAI 2025 (main)

Abstract: Image inpainting aims to restore the original image from a damaged version. Recently, a special type of diffusion bridge model has achieved promising performance by directly mapping the degradation process and restoring corrupted images through the corresponding reverse process. However, due to the lack of explicit semantic priors during the denoising process, the inpainted results typically exhibit inferior context-stability and semantic consistency. To this end, this paper proposes a novel Global Structure-Guided Diffusion Bridge framework (GSGDiff), which incorporates an additional structure restorer to stabilize the generation of holistic semantics. Specifically, to acquire richer semantic structure priors, this paper proposes a posterior sampling approach that captures semantically global and consistent structures at each timestep, efficiently integrating them into the texture generation through the corresponding guidance module. Additionally, considering the characteristics of diffusion models with low denoising levels at larger timesteps, this paper proposes a semantic fusion schedule to avoid noise interference by reducing the weight of ineffective guided semantics in the early stages. By applying the proposed posterior sampling to the texture denoising process, GSGDiff can achieve more stable and superior inpainting results over competitive baselines. Experiments on Places2, Paris Street View and CelebA-HQ datasets validate the efficacy of the proposed method.
Directing Mamba to Complex Textures: An Efficient Texture-Aware State Space Model for Image Restoration
Authors: Long Peng; Xin Di; Zhanfeng Feng; Wenbo Li; Renjing Pei; Yang Wang; Xueyang Fu; Yang Cao; Zheng-Jun Zha
Venue: IJCAI 2025 (main)

Abstract: Image restoration aims to recover details and enhance contrast in degraded images. With the growing demand for high-quality imaging (e.g., 4K and 8K), achieving a balance between restoration quality and computational efficiency has become increasingly critical. Existing methods, primarily based on CNNs, Transformers, or their hybrid approaches, apply uniform deep representation extraction across the image. However, these methods often struggle to effectively model long-range dependencies and largely overlook the spatial characteristics of image degradation (regions with richer textures tend to suffer more severe damage), making it hard to achieve the best trade-off between restoration quality and efficiency. To address these issues, we propose a novel texture-aware image restoration method, TAMambaIR, which simultaneously perceives image textures and achieves a trade-off between performance and efficiency. Specifically, we introduce a novel Texture-Aware State Space Model, which enhances texture awareness and improves efficiency by modulating the transition matrix of the state-space equation and focusing on regions with complex textures. Additionally, we design a Multi-Directional Perception Block to improve multi-directional receptive fields while maintaining low computational overhead. Extensive experiments on benchmarks for image super-resolution, deraining, and low-light image enhancement demonstrate that TAMambaIR achieves state-of-the-art performance with significantly improved efficiency, establishing it as a robust and efficient framework for image restoration.
EyeSeg: An Uncertainty-Aware Eye Segmentation Framework for AR/VR
Authors: Zhengyuan Peng; Jianqing Xu; Shen Li; Jiazhen Ji; Yuge Huang; Jingyun Zhang; Jinmin Li; Shouhong Ding; Rizen Guo; Xin Tan; Lizhuang Ma
Venue: IJCAI 2025 (main)

Abstract: Human-machine interaction through augmented reality (AR) and virtual reality (VR) is increasingly prevalent, requiring accurate and efficient gaze estimation which hinges on the accuracy of eye segmentation to enable smooth user experiences. We introduce EyeSeg, a novel eye segmentation framework designed to overcome key challenges that existing approaches struggle with: motion blur, eyelid occlusion, and train-test domain gaps. In these situations, existing models struggle to extract robust features, leading to suboptimal performance. Noting that these challenges can be generally quantified by uncertainty, we design EyeSeg as an uncertainty-aware eye segmentation framework for AR/VR wherein we explicitly model the uncertainties by performing Bayesian uncertainty learning of a posterior under the closed set prior. Theoretically, we prove that a statistic of the learned posterior indicates segmentation uncertainty levels and empirically outperforms existing methods in downstream tasks, such as gaze estimation. EyeSeg outputs an uncertainty score and the segmentation result, weighting and fusing multiple gaze estimates for robustness, which proves to be effective especially under motion blur, eyelid occlusion and cross-domain challenges. Moreover, empirical results suggest that EyeSeg achieves segmentation improvements of MIoU, E1, F1, and ACC surpassing previous approaches.
Accelerating Diffusion-based Super-Resolution with Dynamic Time-Spatial Sampling
Authors: Rui Qin; Qijie Wang; Ming Sun; Haowei Zhu; Chao Zhou; Bin Wang
Venue: IJCAI 2025 (main)

Abstract: Diffusion models have gained attention for their success in modeling complex distributions, achieving impressive perceptual quality in SR tasks. However, existing diffusion-based SR methods often suffer from high computational costs, requiring numerous iterative steps for training and inference. Existing acceleration techniques, such as distillation and solver optimization, are generally task-agnostic and do not fully leverage the specific characteristics of low-level tasks like super-resolution (SR). In this study, we analyze the frequency- and spatial-domain properties of diffusion-based SR methods, revealing key insights into the temporal and spatial dependencies of high-frequency signal recovery. Specifically, high-frequency details benefit from concentrated optimization during early and late diffusion iterations, while spatially textured regions demand adaptive denoising strategies. Building on these observations, we propose the Time-Spatial-aware Sampling strategy (TSS) for the acceleration of Diffusion SR without any extra training cost. TSS combines Time Dynamic Sampling (TDS), which allocates more iterations to refining textures, and Spatial Dynamic Sampling (SDS), which dynamically adjusts strategies based on image content. Extensive evaluations across multiple benchmarks demonstrate that TSS achieves state-of-the-art (SOTA) performance with significantly fewer iterations, improving MUSIQ scores by 0.2~3.0 and outperforming the current acceleration methods with only half the number of steps.
EfficientPIE: Real-Time Prediction on Pedestrian Crossing Intention with Sole Observation
Authors: Fang Qu; Pengzhan Zhou; Yuepeng He; Kaixin Gao; Youyu Luo; Xin Feng; Yu Liu; Songtao Guo
Venue: IJCAI 2025 (main)

Abstract: Present Advanced Driving Assistance System (ADAS) responds to the dangerous crossing of pedestrians after the occurrence of the incident, occasionally causing severe accidents due to the stringent response window. Inference of pedestrian crossing intention may help vehicles operate in advance and enhance the safety of the vehicle by predicting the crossing probability. Recent studies usually ignore the demand of real-time forecast that required in the realistic driving scenario, and mainly focus on improving the model representation capacity on public datasets by increasing modality and observation time. Consequently, a new framework named EfficientPIE is proposed to predict the pedestrian crossing intention in real time with sole observation of the incident. To achieve reliable predictions, we propose incremental learning based on intention domain to relieve forgetting and promote performance with a progressive perturbation method. Our EfficientPIE outperforms all the SOTA models on two datasets PIE and JAAD, running nearly 7.4x faster than the previously fastest model. Our code is available at https://github.com/heinideyibadiaole/EfficientPIE.
Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance
Authors: Mingcheng Qu; Guang Yang; Donglin Di; Tonghua Su; Yue Gao; Yang Song; Lei Fan
Venue: IJCAI 2025 (main)

Abstract: Multimodal pathology-genomic analysis has become increasingly prominent in cancer survival prediction. However, existing studies mainly utilize multi-instance learning to aggregate patch-level features, neglecting the information loss of contextual and hierarchical details within pathology images. Furthermore, the disparity in data granularity and dimensionality between pathology and genomics leads to a significant modality imbalance. The high spatial resolution inherent in pathology data renders it a dominant role while overshadowing genomics in multimodal integration. In this paper, we propose a multimodal survival prediction framework that incorporates hypergraph learning to effectively capture both contextual and hierarchical details from pathology images. Moreover, it employs a modality rebalance mechanism and an interactive alignment fusion strategy to dynamically reweight the contributions of the two modalities, thereby mitigating the pathology-genomics imbalance. Quantitative and qualitative experiments are conducted on five TCGA datasets, demonstrating that our model outperforms advanced methods by over 3.4% in C-Index performance. Code: https://github.com/MCPathology/MRePath.
ExpTalk: Diverse Emotional Expression via Adaptive Disentanglement and Refined Alignment for Speech-Driven 3D Facial Animation
Authors: Zhan Qu; Shengyu Zhang; Mengze Li; Zhuo Chen; Chengfei Lv; Zhou Zhao; Fei Wu
Venue: IJCAI 2025 (main)

Abstract: Speech-driven 3D facial animation aims to create lifelike facial expressions that synchronize accurately with speech. Despite significant progress, many existing methods may focus on generating facial animation with a fixed emotional state, neglecting the diverse transformations of facial emotions under a given speech input. To solve this issue, we focus on exploring the refined alignment between speech representations and multiple domains in facial expression information. We aim to disentangle the spoken language and emotion facial priors from speech expressions, to guide the refinement of the facial vertices based on speech. To accomplish this objective, we propose ExpTalk, which first applies an Adaptive Disentanglement Variational Autoencoder (AD-VAE) to decouple facial expression aligned with spoken language and emotions of speech through contrastive learning. Then a Refined Alignment Diffusion (RAD) is employed to iteratively refine the decoupled facial expression priors through diffusion-based perturbations, producing facial animations that align with the emotional variations of the given speech. Extensive experiments prove the effectiveness of our ExpTalk by surpassing state-of-the-arts by a large margin.
K-Buffers: A Plug-in Method for Enhancing Neural Fields with Multiple Buffers
Authors: Haofan Ren; Zunjie Zhu; Xiang Chen; Ming Lu; Rongfeng Lu; Chenggang Yan
Venue: IJCAI 2025 (main)

Abstract: Neural fields are now the central focus of research in 3D vision and computer graphics. Existing methods mainly focus on various scene representations, such as neural points and 3D Gaussians. However, few works have studied the rendering process to enhance the neural fields. In this work, we propose a plug-in method named K-Buffers that leverages multiple buffers to improve the rendering performance. Our method first renders K buffers from scene representations and constructs K pixel-wise feature maps. Then, We introduce a K-Feature Fusion Network (KFN) to merge the K pixel-wise feature maps. Finally, we adopt a feature decoder to generate the rendering image. We also introduce an acceleration strategy to improve rendering speed and quality. We apply our method to well-known radiance field baselines, including neural point fields and 3D Gaussian Splatting (3DGS). Extensive experiments demonstrate that our method effectively enhances the rendering performance of neural point fields and 3DGS.
Q-MiniSAM2: A Quantization-based Benchmark for Resource-Efficient Video Segmentation
Authors: Xuanxuan Ren; Xiangyu Li; Kun Wei; Xu Yang; Yanhua Yang
Venue: IJCAI 2025 (main)

Abstract: Segment Anything Model 2 (SAM2) is a new-generation, high-precision model for image and video segmentation, offering extensive application prospects across numerous computer vision fields. However, as a large-scale model, its huge memory demands and expansive computing costs pose challenges for practical deployment. This paper presents Q-MiniSAM2, an efficient Quantization-based segmentation benchmark tailored to optimize SAM2 by Minimizing memory consumption and accelerating computations. We begin with applying Post-Training Quantization (PTQ) to SAM2, requiring only a relatively small dataset for network calibration, thereby eliminating the need for retraining. Building upon PTQ, we further introduce a Hierarchy-based Video Quantization method to enhance the model’s capacity to capture video semantics and temporal correlations across different time scales. Furthermore, we observe that SAM2’s memory overhead is predominantly concentrated on processing historical frames, and the redundant cross-attention computations significantly increase memory and computational costs due to the imperceptible change of the short time intervals between these frames. To tackle this issue, an Adaptive Mutual-KV mechanism is proposed to mitigate excessive cross-attention by leveraging inter-frame similarities. Comprehensive experiments demonstrate that the proposed approach achieves superior performance compared to state-of-the-art methods, underscoring its potential for efficient and scalable video segmentation.
RDPA: Real-Time Distributed-Concentrated Penetration Attack for Point Cloud Learning
Authors: Youtong Shi; Lixin Chen; Yu Zang; Chenhui Yang; Cheng Wang
Venue: IJCAI 2025 (main)

Abstract: Partial point attack approaches focus on leveraging the fewest points to achieve the best attack efficiency for easy implementation in the physical domain. For the first time, this paper proposes that the partial point attack strategy should pay attention to not only the selection and disturbance of points, but also the penetration of current defense methods. By re-examining characteristics of previous partial point attack approaches leading to performance improvement, we discover two fundamental principles: first, the selection of attacked points should consider not only the favourable visual salience but also the proper position concentration, thus to acquire effective structural destruction on the basis of remaining imperceptible; second, the perturbation of target points should form meaningful structures rather than outliers. To achieve this, we first propose a novel distributed-concentrated point selection (DPS) strategy, which is easier to concentrate salient points containing rich local information in a few tiny regions. Additionally, to enhance the penetration efficacy and real-time performance of attack point clouds against defenses, we further design a perturbation network based on the multi-scale penetration loss (L_msp), which can generate adversarial samples with as few outliers as possible only through a single forward propagation. Experimental results demonstrate that the real-time distributed-concentrated penetration attack (RDPA) framework can achieve state-of-the-art (SOTA) success rates by perturbing only 3.5% of points, and have the best penetration for mainstream defense methods such as SRS and SOR.
BankTweak: Adversarial Attack Against Multi-Object Trackers by Manipulating Feature Banks
Authors: Woojin Shin; Donghwa Kang; Daejin Choi; Brent Byunghoon Kang; Jinkyu Lee; Hyeongboo Baek
Venue: IJCAI 2025 (main)

Abstract: Modern multi-object tracking (MOT) predominantly relies on the tracking-by-detection paradigm to construct object trajectories. Traditional MOT attacks primarily degrade detection quality in specific frames only, lacking efficiency, while state-of-the-art (SOTA) approaches induce persistent identity (ID) switches by manipulating object positions during the association phase, even after the attack ends. In this paper, we reveal that these SOTA attacks can be easily counteracted by adjusting distance-related parameters in the association phase, exposing their lack of robustness. To overcome these limitations, we propose BankTweak, a novel adversarial attack targeting feature-based MOT systems to induce persistent ID switches (efficiency) without modifying object positions (robustness). BankTweak exploits a critical vulnerability in the Hungarian matching algorithm of MOT systems by strategically injecting altered features into feature banks during the association phase. Extensive experiments on MOT17 and MOT20 datasets, combining various detectors, feature extractors, and trackers, demonstrate that BankTweak significantly outperforms SOTA attacks up to 11.8 times, exposing fundamental vulnerabilities in the tracking-by-detection framework.