GenAI4ELab/papercli-papers huggingface dataset:
Correspondence-Free Fast and Robust Spherical Point Pattern Registration
Authors: Anik Sarker; Alan T. Asbeck
Venue: ICCV 2025 (main)

Abstract: Existing methods for rotation estimation between two spherical (\mathbb S ^2) patterns typically rely on spherical cross-correlation maximization between two spherical function. However, these approaches exhibit computational complexities greater than cubic O(n^3) with respect to rotation space discretization and lack extensive evaluation under significant outlier contamination.To this end, we propose a rotation estimation algorithm between two spherical patterns with linear time complexity O(n). Unlike existing spherical-function-based methods, we explicitly represent spherical patterns as discrete 3D point sets on the unit sphere, reformulating rotation estimation as a spherical point-set alignment (i.e., Wahba problem for 3D unit vectors). Given the geometric nature of our formulation, our spherical pattern alignment algorithm naturally aligns with the Wahba problem framework for 3D unit vectors. Specifically, we introduce three novel algorithms: (1) SPMC (Spherical Pattern Matching by Correlation), (2) FRS (Fast Rotation Search), and (3) a hybrid approach (SPMC+FRS) that combines the advantages of the previous two methods. Our experiments demonstrate that in the \mathbb S ^2 domain and in correspondence-free settings, our algorithms are over 10x faster and over 10x more accurate than current state-of-the-art methods for the Wahba problem with outliers. We validate our approach through extensive simulations on a new dataset of spherical patterns, the "Robust Vector Alignment Dataset."Furthermore, we adapt our methods to two real-world tasks: (i) Point Cloud Registration (PCR) and (ii) rotation estimation for spherical images. In the PCR task, our approach successfully registers point clouds exhibiting overlap ratios as low as 65%. In spherical image alignment, we show that our method robustly estimates rotations even under challenging conditions involving substantial clutter (over 19%) and large rotational offsets. Our results highlight the effectiveness and robustness of our algorithms in realistic, complex scenarios.Our Code is available in the attached link: https://anonymous.4open.science/r/RobustVectorAlignment-EC0E/README.md
Adversarial Robustness of Discriminative Self-Supervised Learning in Vision
Authors: Ömer Veysel Çağatan; Ömer Faruk Tal; M. Emre Gursoy
Venue: ICCV 2025 (main)

Abstract: Self-supervised learning (SSL) has advanced significantly in visual representation learning, yet comprehensive evaluations of its adversarial robustness remain limited. In this study, we evaluate the adversarial robustness of seven discriminative self-supervised models and one supervised model across diverse tasks, including ImageNet classification, transfer learning, segmentation, and detection. Our findings suggest that discriminative SSL models generally exhibit better robustness to adversarial attacks compared to their supervised counterpart on ImageNet, with this advantage extending to transfer learning when using linear evaluation. However, when fine-tuning is applied, the robustness gap between SSL and supervised models narrows considerably. Similarly, this robustness advantage diminishes in segmentation and detection tasks. We also investigate how various factors might influence adversarial robustness, including architectural choices, training duration, data augmentations, and batch sizes. Our analysis contributes to the ongoing exploration of adversarial robustness in visual self-supervised representation systems.
CT-ScanGaze: A Dataset and Baselines for 3D Volumetric Scanpath Modeling
Authors: Trong Thang Pham; Akash Awasthi; Saba Khan; Esteban Duran Marti; Tien-Phat Nguyen; Khoa Vo; Minh Tran; Son Nguyen; Cuong Tran; Yuki Ikebe; Anh Totti Nguyen; Anh Nguyen; Zhigang Deng; Carol C. Wu; Hien Nguyen; Ngan Le
Venue: ICCV 2025 (main)

Abstract: Understanding radiologists' eye movement during Computed Tomography (CT) reading is crucial for developing effective interpretable computer-aided diagnosis systems. However, CT research in this area has been limited by the lack of publicly available eye-tracking datasets and the three-dimensional complexity of CT volumes. To address these challenges, we present the first publicly available eye gaze dataset on CT, called CT-ScanGaze. Then, we introduce CT-Searcher, a novel 3D scanpath predictor designed specifically to process CT volumes and generate radiologist-like 3D fixation sequences, overcoming the limitations of current scanpath predictors that only handle 2D inputs. Since deep learning models benefit from a pretraining step, we develop a pipeline that converts existing 2D gaze datasets into 3D gaze data to pretrain CT-Searcher. Through both qualitative and quantitative evaluations on CT-ScanGaze, we demonstrate the effectiveness of our approach and provide a comprehensive assessment framework for 3D scanpath prediction in medical imaging.
MOBIUS: Big-to-Mobile Universal Instance Segmentation via Multi-modal Bottleneck Fusion and Calibrated Decoder Pruning
Authors: Mattia Segu; Marta Tintore Gazulla; Yongqin Xian; Luc Van Gool; Federico Tombari
Venue: ICCV 2025 (main)

Abstract: Scaling up model size and training data has advanced foundation models for instance-level perception, achieving state-of-the-art in-domain and zero-shot performance across object detection and segmentation. However, their high computational cost limits adoption on resource-constrained platforms. We first examine the limitations of existing architectures in enabling efficient edge deployment without compromising performance. We then introduce MOBIUS, a family of foundation models for universal instance segmentation, designed for Pareto-optimal downscaling to support deployment across devices ranging from high-end accelerators to mobile hardware. To reduce training and inference demands, we propose: (i) a bottleneck pixel decoder for efficient multi-scale and multi-modal fusion, (ii) a language-guided uncertainty calibration loss for adaptive decoder pruning, and (iii) a streamlined, unified training strategy. Unlike efficient baselines that trade accuracy for reduced complexity, MOBIUS reduces pixel and transformer decoder FLOPs by up to 55% and 75%, respectively, while maintaining state-of-the-art performance in just a third of the training iterations. MOBIUS establishes a new benchmark for efficient segmentation on both high-performance computing platforms and mobile devices.
VoxelKP: A Voxel-based Network Architecture for Human Keypoint Estimation in LiDAR Data
Authors: Jian Shi; Peter Wonka
Venue: ICCV 2025 (main)

Abstract: We present VoxelKP, a novel fully sparse network architecture tailored for human keypoint estimation in LiDAR data. The key challenge is that objects are distributed sparsely in 3D space, while human keypoint detection requires detailed local information wherever humans are present. First, we introduce a dual-branch fully sparse spatial-context block where the spatial branch focuses on learning the local spatial correlations between keypoints within each human instance, while the context branch aims to retain the global spatial information. Second, we use a spatially aware multi-scale BEV fusion technique to leverage absolute 3D coordinates when projecting 3D voxels to a 2D grid encoding a bird's eye view for better preservation of the global context of each human instance. We evaluate our method on the Waymo dataset and achieve an improvement of 27% on the MPJPE metric compared to the state-of-the-art, HUM3DIL, trained on the same data, and 12% against the state-of-the-art, GC-KPL, pretrained on a 25xlarger dataset. To the best of our knowledge, VoxelKP is the first single-staged, fully sparse network that is specifically designed for addressing the challenging task of 3D keypoint estimation from LiDAR data, achieving state-of-the-art performance. Our code is available at https://github.com/shijianjian/VoxelKP.
TemCoCo: Temporally Consistent Multi-modal Video Fusion with Visual-Semantic Collaboration
Authors: Meiqi Gong; Hao Zhang; Xunpeng Yi; Linfeng Tang; Jiayi Ma
Venue: ICCV 2025 (main)

Abstract: Existing multi-modal fusion methods typically apply static frame-based image fusion techniques directly to video fusion tasks, neglecting inherent temporal dependencies and leading to inconsistent results across frames. To address this limitation, we propose the first video fusion framework that explicitly incorporates temporal modeling with visual-semantic collaboration to simultaneously ensure visual fidelity, semantic accuracy, and temporal consistency. First, we introduce a visual-semantic interaction module consisting of a semantic branch and a visual branch, with Dinov2 and VGG19 employed for targeted distillation, allowing simultaneous enhancement of both the visual and semantic representations. Second, we pioneer integrate the video degradation enhancement task into the video fusion pipeline by constructing a temporal cooperative module, which leverages temporal dependencies to facilitate weak information recovery. Third, to ensure temporal consistency, we embed a temporal-enhanced mechanism into the network and devise a temporal loss to guide the optimization process. Finally, we introduce two innovative evaluation metrics tailored for video fusion, aimed at assessing the temporal consistency of the generated fused videos. Extensive experimental results on public video datasets demonstrate the superiority of our method. Our code is released at https://github.com/Meiqi-Gong/TemCoCo.
Normal and Abnormal Pathology Knowledge-Augmented Vision-Language Model for Anomaly Detection in Pathology Images
Authors: Jinsol Song; Jiamu Wang; Anh Tien Nguyen; Keunho Byeon; Sangjeong Ahn; Sung Hak Lee; Jin Tae Kwak
Venue: ICCV 2025 (main)

Abstract: Anomaly detection in computational pathology aims to identify rare and scarce anomalies where disease-related data are often limited or missing. Existing anomaly detection methods, primarily designed for industrial settings, face limitations in pathology due to computational constraints, diverse tissue structures, and lack of interpretability. To address these challenges, we propose Ano-NAViLa, a Normal and Abnormal pathology knowledge-augmented Vision-Language model for Anomaly detection in pathology images. Ano-NAViLa is built on a pre-trained vision-language model with a lightweight trainable MLP. By incorporating both normal and abnormal pathology knowledge, Ano-NAViLa enhances accuracy and robustness to variability in pathology images and provides interpretability through image-text associations. Evaluated on two lymph node datasets from different organs, Ano-NAViLa achieves the state-of-the-art performance in anomaly detection and localization, outperforming competing models.
COME: Dual Structure-Semantic Learning with Collaborative MoE for Universal Lesion Detection Across Heterogeneous Ultrasound Datasets
Authors: Lingyu Chen; Yawen Zeng; Yue Wang; Peng Wan; Guochen Ning; Hongen Liao; Daoqiang Zhang; Fang Chen
Venue: ICCV 2025 (main)

Abstract: Conventional single-dataset training often fails with new data distributions, especially in ultrasound (US) image analysis due to limited data, acoustic shadows, and speckle noise.Therefore, constructing a universal framework for multi-heterogeneous US datasets is imperative. However, a key challenge arises: how to effectively mitigate inter-dataset interference while preserving dataset-specific discriminative features for robust downstream task? Previous approaches utilize either a single source-specific decoder or a domain adaptation strategy, but these methods experienced a decline in performance when applied to other domains. Considering this, we propose a Universal Collaborative Mixture of Heterogeneous Source-Specific Experts (COME). Specifically, COME establishes dual structure-semantic shared experts that create a universal representation space and then collaborate with source-specific experts to extract discriminative features through providing complementary features. This design enables robust generalization by leveraging cross-datasets experience distributions and providing universal US priors for small-batch or unseen data scenarios. Extensive experiments under three evaluation modes (single-dataset, intra-organ, and inter-organ integration datasets) demonstrate COME's superiority, achieving significant mean AP improvements over state-of-the-art methods.
Fine-Tuning Visual Autogressive Models for Subject-Driven Generation
Authors: Jiwoo Chung; Sangeek Hyun; Hyunjun Kim; Eunseo Koh; MinKyu Lee; Jae-Pil Heo
Venue: ICCV 2025 (main)

Abstract: Recent advances in text-to-image generative models have enabled numerous practical applications, including subject-driven generation, which fine-tunes pre-trained models to capture subject semantics from only a few examples. While diffusion-based models produce high-quality images, their extensive denoising steps result in significant computational overhead, limiting real-world applicability.Visual Auto-Regressive (VAR) models, which predict next-scale tokens rather than spatially adjacent ones, offer significantly faster inference suitable for practical deployment. In this paper, we propose the first VAR-based approach for subject-driven generation. However, naive fine-tuning VAR leads to computational overhead, language drift, and reduced diversity. To address these challenges, we introduce selective layer tuning to reduce complexity and prior distillation to mitigate language drift. Additionally, we found that the early stages have a greater influence on the generation of subject than the latter stages, which merely synthesize local details. Based on this finding, we propose scale-wise weighted tuning, which prioritizes coarser resolutions for promoting the model to focus on the subject-relevant information instead of local details. Extensive experiments validate that our method significantly outperforms diffusion-based baselines across various metrics and demonstrates its practical usage.
Voyaging into Perpetual Dynamic Scenes from a Single View
Authors: Fengrui Tian; Tianjiao Ding; Jinqi Luo; Hancheng Min; Rene Vidal
Venue: ICCV 2025 (main)

Abstract: The problem of generating a perpetual dynamic scene from a single view is an important problem with widespread applications in augmented and virtual reality, and robotics. However, since dynamic scenes regularly change over time, a key challenge is to ensure that different generated views be consistent with the underlying 3D motions. Prior work learns such consistency by training on multiple views, but the generated scene regions often interpolate between training views and fail to generate perpetual views. To address this issue, we propose DynamicVoyager, which reformulates dynamic scene generation as a scene outpainting problem with new dynamic content. As 2D outpainting models struggle at generating 3D consistent motions from a single 2D view, we enrich 2D pixels with information from their 3D rays that facilitates learning of 3D motion consistency. More specifically, we first map the single-view video input to a dynamic point cloud using the estimated video depths. We then render a partial video of the point cloud from a novel view and outpaint the missing regions using ray information (e.g., the distance from a ray to the point cloud) to generate 3D consistent motions. Next, we use the outpainted video to update the point cloud, which is used for outpainting the scene from future novel views. Moreover, we can control the generated content with the input text prompt. Experiments show that our model can generate perpetual scenes with consistent motions along fly-through cameras. Project page: https://tianfr.github.io/DynamicVoyager
Accelerating Diffusion Transformer via Gradient-Optimized Cache
Authors: Junxiang Qiu; Lin Liu; Shuo Wang; Jinda Lu; Kezhou Chen; Yanbin Hao
Venue: ICCV 2025 (main)

Abstract: Feature caching has emerged as an effective strategy to accelerate diffusion transformer (DiT) sampling through temporal feature reuse. It is a challenging problem since (1) Progressive error accumulation from cached blocks significantly degrades generation quality, particularly when over 50% of blocks are cached; (2) Current error compensation approaches neglect dynamic perturbation patterns during the caching process, leading to suboptimal error correction. To solve these problems, we propose the Gradient-Optimized Cache (GOC) with two key innovations: (1) Cached Gradient Propagation: A gradient queue dynamically computes the gradient differences between cached and recomputed features. These gradients are weighted and propagated to subsequent steps, directly compensating for the approximation errors introduced by caching. (2) Inflection-Aware Optimization: Through statistical analysis of feature variation patterns, we identify critical inflection points where the denoising trajectory changes direction. By aligning gradient updates with these detected phases, we prevent conflicting gradient directions during error correction. Extensive evaluations on ImageNet demonstrate GOC's superior trade-off between efficiency and quality. With 50% cached blocks, GOC achieves IS 216.28 (26.3%|) and FID 3.907 (43%|) compared to baseline DiT, while maintaining identical computational costs. These improvements persist across various cache ratios, demonstrating robust adaptability to different acceleration requirements. Code is available at https://github.com/qiujx0520/GOC_ICCV2025.git.
SceneSplat: Gaussian Splatting-based Scene Understanding with Vision-Language Pretraining
Authors: Yue Li; Qi Ma; Runyi Yang; Huapeng Li; Mengjiao Ma; Bin Ren; Nikola Popovic; Nicu Sebe; Ender Konukoglu; Theo Gevers; Luc Van Gool; Martin R. Oswald; Danda Pani Paudel
Venue: ICCV 2025 (main)

Abstract: Recognizing arbitrary or previously unseen categories is essential for comprehensive real-world 3D scene understanding. Currently, all existing methods rely on 2D or textual modalities during training, or together at inference. This highlights a clear absence of a model capable of processing 3D data alone for learning semantics end-to-end, along with the necessary data to train such a model. Meanwhile, 3D Gaussian Splatting (3DGS) has emerged as the de facto standard for 3D scene representation across various vision tasks. However, effectively integrating semantic reasoning into 3DGS in a generalizable fashion remains an open challenge.To address these limitations we introduce SceneSplat, to our knowledge the first large-scale 3D indoor scene understanding approach that operates natively on 3DGS. Furthermore, we propose a self-supervised learning scheme that unlocks rich 3D feature learning from unlabeled scenes. In order to power the proposed methods, we introduce SceneSplat-7K, the first large-scale 3DGS dataset for indoor scenes, comprising of 6868 scenes derived from 7 established datasets like ScanNet, Matterport3D, etc. Generating SceneSplat-7K required computational resources equivalent to 119 GPU-days on an L4 GPU, enabling standardized benchmarking for 3DGS-based reasoning for indoor scenes.Our exhaustive experiments on SceneSplat-7K demonstrate the significant benefit of the proposed methods over the established baselines. Our code, model, and datasets will be released to facilitate further research.
Beyond the Limits: Overcoming Negative Correlation of Activation-Based Training-Free NAS
Authors: Haidong Kang; Lianbo Ma; Pengjun Chen; Guo Yu; Xingwei Wang; Min Huang
Venue: ICCV 2025 (main)

Abstract: Training-free Neural Architecture Search (NAS) has emerged an efficient way to discover high-performing lightweight models with zero-cost proxies (e.g., the activation-based proxies (AZP)). In this paper, we observe a new negative correlation phenomenon that the correlations of the AZP dramatically decrease to be negative with the increasing number of convolutions, significantly degrading the prediction performance of AZP over target architectures. No existing works focus on such negative correlation and its underlying mechanism. To address this, through deep analysis of the architectural characteristics scored by AZP, we propose a series of AZP design principles and reveal the potential reason of the above phenomenon that high non-linearity dramatically degrades the magnitude of AZP score. Those findings show that existing AZP designs do not obey the proposed principles. Finally, grounded in these insights, we propose a simple yet efficient \underline N egative \underline C orrelations-\underline D efied (NCD) method, which utilize stochastic activation masking (SAM) and non-linear rescaling (NIR) to effectively eliminate negative correlation of AZP and significantly improve performance. Extensive experimental results validate the effectiveness and efficiency of our method, outperforming state-of-the-art methods on mainstream 12 search spaces with 4 real-world tasks.
Unknown Text Learning for CLIP-based Few-Shot Open-set Recognition
Authors: Rui Ma; Qilong Wang; Bing Cao; Qinghua Hu; Yahong Han
Venue: ICCV 2025 (main)

Abstract: Recently, vision-language models (e.g., CLIP) with prompt learning have shown great potential in few-shot learning. However, an open issue remains for the effective extension of CLIP-based models to few-shot open-set recognition (FSOR), which requires classifying known classes and detecting unknown samples using a few known samples. The core challenge is that unknown samples and their textual descriptions are unavailable. To address this, we propose an Unknown Text Learning (UTL) method for CLIP-based FSOR tasks with only known samples. Specifically, UTL involves two key components, i.e., universal unknown words optimization (U^ 2 WO) and unknown label smoothing (ULS). Specifically, U^ 2 WO constructs the universal space of unknown words with basis vectors and characterizes unknown text based on a linear combination of those basis vectors. To efficiently learn unknown text without unknown samples, ULS is presented to perform contrast learning between unknown text and known samples by regulating the label of unknown classes to a small constant, which flexibly empowers unknown text to be non-matching with and confused on known visual samples. In addition, our UTL incorporates an additional context for known classes to mitigate conflicts of context optimization between known and unknown classes. UTL effectively regularizes the predicted probability by integrating learnable unknown text. Experimental results on various benchmarks show that our UTL is superior to its counterparts while achieving state-of-the-art performance.
VoteSplat: Hough Voting Gaussian Splatting for 3D Scene Understanding
Authors: Minchao Jiang; Shunyu  Jia; Jiaming Gu; Xiaoyuan Lu; Guangming Zhu; Anqi  Dong; Liang  Zhang
Venue: ICCV 2025 (main)

Abstract: 3D Gaussian Splatting (3DGS) has become horsepower in high-quality, real-time rendering for novel view synthesis of 3D scenes. However, existing methods focus primarily on geometric and appearance modeling, lacking deeper scene understanding while also incurring high training costs that complicate the originally streamlined differentiable rendering pipeline. To this end, we propose VoteSplat, a novel 3D scene understanding framework that integrates Hough voting with 3DGS. Specifically, Segment Anything Model (SAM) is utilized for instance segmentation, extracting objects, and generating 2D vote maps. We then embed spatial offset vectors into Gaussian primitives. These offsets construct 3D spatial votes by associating them with 2D image votes, while depth distortion constraints refine localization along the depth axis. For open-vocabulary object localization, VoteSplat maps 2D image semantics to 3D point clouds via voting points, reducing training costs associated with high-dimensional CLIP features while preserving semantic unambiguity. Extensive experiments demonstrate VoteSplat's effectiveness in open-vocabulary 3D instance localization, 3D point cloud understanding, click-based 3D object localization, hierarchical segmentation, and ablation studies.