Few-shot industrial anomaly detection (FS-IAD) presents a critical challenge for practical automated inspection systems operating in data-scarce environments. While existing approaches predominantly focus on obtaining prototypes from limited normal images, they neglect to systematically incorporate statistics of query image to enhance prototype representativeness. To address this issue, we propose FastRef, a novel and efficient prototype refinement framework for FS-IAD. Our method operates through an iterative two-stage process during inference: (1) characteristic transfer from query features to the enhanced prototypes, and (2) anomaly suppression by aligning the enhanced prototypes with their normal counterparts. The characteristic transfer is achieved through linear reconstruction of query features from prototypes with an optimizable transport matrix, while the anomaly suppression addresses a key observation in FS-IAD that unlike conventional IAD with abundant normal prototypes, the limited-sample setting makes anomaly reconstruction more probable in characteristic transfer. Therefore, we employ optimal transport to measure while minimize the gap between prototypes and their enhanced counterparts for anomaly suppression. For comprehensive evaluation, we integrate FastRef with three competitive prototype-based FS-IAD methods: PatchCore, WinCLIP, and AnomalyDINO. Extensive experiments across four benchmark datasets of MVTec, ViSA, MPDD and RealIAD demonstrate both the effectiveness and efficiency of our approach under 1/2/4-shots.
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Test-Time Adaptation (TTA) is essential for enabling deep learning models to handle real-world data distribution shifts. However, current approaches face significant limitations: backpropagation-based methods are not suitable for low-end deployment devices, due to their high computation and memory requirements, as well as their tendency to modify model weights during adaptation; while traditional backpropagation-free techniques exhibit constrained adaptation capabilities. In this work, we propose Forward-Only Zeroth-Order Optimization (FOZO), a novel and practical backpropagation-free paradigm for TTA. FOZO leverages a memory-efficient zeroth-order prompt optimization, which is led by objectives optimizing both intermediate feature statistics and prediction entropy. To ensure efficient and stable adaptation over the out-of-distribution data stream, we introduce a dynamically decaying perturbation scale during zeroth-order gradient estimation and theoretically prove its convergence under the TTA data stream assumption. Extensive continual adaptation experiments on ImageNet-C, ImageNet-R, and ImageNet-Sketch demonstrate FOZO's superior performance, achieving 59.52% Top-1 accuracy on ImageNet-C (5K, level 5) and outperforming main gradient-based methods and SOTA forward-only FOA (58.13%). Furthermore, FOZO exhibits strong generalization on quantized (INT8) models. These findings demonstrate that FOZO is a highly competitive solution for TTA deployment in resource-limited scenarios. Code: https://github.com/eVI-group-SCU/FOZO
We study monocular metric depth estimation (MMDE) without camera intrinsics at training or inference. When focal length and scene depth vary together, depth changes are difficult to perceive in the image, yet the edge-frequency statistics exhibit systematic, scale-correlated shifts. Building on this observation, we introduce a spectral quantile estimator (SQE) that analyzes the Fourier spectrum of a predicted edge map and outputs a single score used as a proxy for metric scale. Consequently, we propose MD2E, a method that models depth-to-edge cues by deriving edge targets from depth annotations, calibrating metric scale using the spectral score, and using edge predictions to regularize depth boundaries while producing metric depth. Across diverse cameras and datasets, MD2E achieves state-of-the-art performance in MMDE in both zero-shot and fine-tuning settings. The project page is available at https://2j472no.github.io/MD2E/.
Prompt-based continual learning methods effectively mitigate catastrophic forgetting. However, most existing methods assign a fixed set of prompts to each task, completely isolating knowledge across tasks and resulting in suboptimal parameter utilization. To address this, we consider the practical needs of continual learning and propose a prompt-sharing framework. This framework constructs a global prompt pool and introduces a task-aware gated routing mechanism that sparsely activates a subset of prompts to achieve dynamic decoupling and collaborative optimization of task-specific feature representations. Furthermore, we introduce a history-aware modulator that leverages cumulative prompt activation statistics to protect frequently used prompts from excessive updates, thereby mitigating inefficient parameter usage and knowledge forgetting. Extensive analysis and empirical results demonstrate that our approach consistently outperforms existing static allocation strategies in effectiveness and efficiency. Code and models will be released.
Reconstructing the three-dimensional distribution of dark matter from weak-lensing observations is a central but highly ill-posed inverse problem in cosmology. Unlike standard 3D reconstruction with multiple viewpoints, we observe the universe from a single line of sight, through noisy shape distortions of galaxies with uncertain distances, so meaningful recovery of the 3D matter field requires strong prior assumptions. Existing methods either produce point estimates with handcrafted priors or use neural ensembles for approximate Bayesian uncertainty, and struggle to capture the non-Gaussian, filamentary structure of the cosmic web. With the advent of new high-resolution cosmological simulations, we now have an alternative source of prior knowledge that captures the nonlinear statistics of structure formation with far greater fidelity than analytic prescriptions. We leverage these simulations to build a new dataset \texttt Conicus3D , which enables us to learn a data-driven diffusion-model prior capturing the full 3D distribution of dark matter structure across cosmic time. Building on recent plug-and-play approaches, we modify a diffusion-based posterior sampling scheme to the 3D weak-lensing setting, combining the learned prior with a differentiable physical forward model. On realistic simulations targeting a modern weak lensing survey, our approach yields substantially improved 2D and 3D reconstruction accuracy over baseline methods. Moreover, it produces posterior samples whose statistics closely track the underlying simulations, while remaining robust to moderate shifts in cosmology.
Cross-modal biomedical signals such as pathology and genomics can provide richer and more robust semantic guidance for medical image representation learning. However, the availability of such guidance remains limited, as privacy constraints and acquisition costs severely restrict access to medical images paired with other biomedical data. A further challenge lies in modality discrepancy, which introduces intra-modal statistical bias and cross-modal noise, thereby degrading the quality of medical image representations. To address these challenges, we propose KAMP, a large language model (LLM)-driven multimodal pretraining framework for medical image representation learning. KAMP leverages textual priors as the semantic anchor to enhance medical image representations and align them with multimodal biomedical representations, enabling the learning of rich and robust features even when paired data are scarce. KAMP operates in three stages. First, the LLM generates personalized diagnostic knowledge from patient clinical text and imaging metadata. This knowledge is injected as a prior to enrich medical image representations and serves as a semantic anchor to reduce the representation gap between medical images and other biomedical modalities. Second, the LLM is optimized using Group Relative Policy Optimization (GRPO), with the cross-modal aligner pretrained in the first stage serving as the reward model. Third, the refined knowledge is used to retrain the cross-modal aligner, yielding more robust medical image representations while mitigating bias and noise introduced by other modalities. Comprehensive evaluations on brain, bladder, and liver cancer datasets demonstrate that KAMP outperforms existing methods in most downstream few-shot classification tasks.
Federated Learning (FL) enables collaborative model training while preserving data privacy, but its practical deployment is hampered by system and statistical heterogeneity. While federated network pruning offers a path to mitigate these issues, existing methods face a critical dilemma: server-side pruning lacks personalization, whereas client-side pruning is computationally prohibitive for resource-constrained devices. Furthermore, the pruning process itself induces significant parametric divergence among heterogeneous submodels, destabilizing training and hindering global convergence. To address these challenges, we propose SubFLOT, a novel framework for server-side personalized federated pruning. SubFLOT introduces an Optimal Transport-enhanced Pruning (OTP) module that treats historical client models as proxies for local data distributions, formulating the pruning task as a Wasserstein distance minimization problem to generate customized submodels without accessing raw data. Concurrently, to counteract parametric divergence, our Scaling-based Adaptive Regularization (SAR) module adaptively penalizes a submodel's deviation from the global model, with the penalty's strength scaled by the client's pruning rate. Comprehensive experiments demonstrate that SubFLOT consistently and substantially outperforms state-of-the-art methods, underscoring its potential for deploying efficient and personalized models on resource-constrained edge devices.
Test-Time Training for LiDAR Semantic Segmentation under Corruption via Geometric Inlier Discrimination
PDF ↗LiDAR semantic segmentation must remain robust under various sensor and environmental corruptions to be reliable in safety-critical applications. Existing test-time adaptation methods, including approaches based on pseudo-labels and normalization statistics, have shown promising results but can still struggle under severe distribution shifts. To complement these approaches, we propose a geometry-aware test-time training framework that leverages an auxiliary self-supervised objective. Our method is based on geometric inlier discrimination (GeoID), which injects synthetic off-manifold points into the input and trains the model to distinguish geometry-consistent inliers from synthetically displaced outliers, enabling adaptation on unlabeled test data. To further stabilize this process under real corruptions, we introduce bidirectional unreliable point filtering (BiUPF), which uses inlier scores from the source-trained model to filter out unreliable regions on both original and synthetic points, focusing updates on high-confidence samples. Experiments on two large-scale corruption benchmarks, SemanticKITTI-C and nuScenes-C, show that our method consistently outperforms strong test-time adaptation baselines and improves robustness across diverse LiDAR corruptions. Our code is available at https://github.com/hskim617/GeoID.
Blind-spot networks (BSNs) enable self-supervised image denoising by preventing access to the target pixel, allowing clean signal estimation without ground-truth supervision. However, this approach assumes pixel-wise noise independence, which is violated in real-world sRGB images due to spatially correlated noise from the camera's image signal processing (ISP) pipeline. While several methods employ downsampling to decorrelate noise, they alter noise statistics and limit the network's ability to utilize full contextual information. In this paper, we propose the Triangular-Masked Blind-Spot Network (TM-BSN), a novel blind-spot architecture that accurately models the spatial correlation of real sRGB noise. This correlation originates from demosaicing, where each pixel is reconstructed from neighboring samples with spatially decaying weights, resulting in a diamond-shaped pattern. To align the receptive field with this geometry, we introduce a triangular-masked convolution that restricts the kernel to its upper-triangular region, creating a diamond-shaped blind spot at the original resolution. This design excludes correlated pixels while fully leveraging uncorrelated context, eliminating the need for downsampling or post-processing. Furthermore, we use knowledge distillation to transfer complementary knowledge from multiple blind-spot predictions into a lightweight U-Net, improving both accuracy and efficiency. Extensive experiments on real-world benchmarks demonstrate that our method achieves state-of-the-art performance, significantly outperforming existing self-supervised approaches. Our code is available at https://github.com/parkjun210/TM-BSN.
FedMOP: Achieving Enhanced Privacy and Performance in Federated Learning via Momentum Orthogonal Projection
PDF ↗Federated Learning (FL) faces a fundamental dilemma: existing defenses against gradient leakage attacks (GLAs) invariably sacrifice model performance for privacy protection through noise injection or gradient clip. We introduce Federated Learning with Momentum-Based Orthogonal Projection (FedMOP), a method that simultaneously achieves strong privacy guarantees and superior model performance. The key insight is to leverage initialization-based offset mechanisms that operate on orthogonal dimensions. For performance enhancement, FedMOP employs gradient orthogonal projection to counteract local drift, effectively offsetting each client's round-training initial model using global statistical context. For privacy protection, it introduces momentum-based trajectory offset hiding, which makes the offset vector inherently unrecoverable by constructing information barriers through private initialization and randomized evolution. These two mechanisms are synergistic rather than antagonistic. Theoretically, we prove convergence preservation and characterize the computationally infeasible inverse problem faced by attackers. Extensive experiments on CIFAR-10/100 and Tiny-ImageNet demonstrate that FedMOP not only defends effectively against state-of-the-art GLAs but also surpasses existing FL methods in both accuracy and convergence speed, validating its ability to jointly enhance privacy and performance in FL. Codes are available at https://github.com/zyl123456aB/FedMOP.
Video crowd counting aims to predict the people count in each frame of a video. It requires effectively leveraging spatio-temporal (ST) information in videos while satisfying real-time constraints. However, most existing methods use ST information from neighboring frames through auxiliary extraction and fusion modules---resulting in large computational cost and the need to buffer multiple frames during inference. Such designs limit their practicality in real-world applications with limited computational resources or stringent real-time requirements. To address these issues, we revisit video crowd counting from the perspective of lightweight image-based counting models that enable real-time deployment under limited resources. We analytically define ST information in a model-independent and statistically interpretable manner, and incorporate it into training via a statistical regularizer that effectively enhances model performance without adding modules or inference overhead. Most framework hyperparameters are further formulated as statistical inference problems, allowing automatic estimation from data and consequently efficient adaptation to new scenarios.Our framework unifies video crowd counting and image-based counting models under a compact, principled formulation that is lightweight, portable, and efficient. We also establish theoretical foundations for adapting image-based counting models to video crowd counting and achieve state-of-the-art accuracy and efficiency across six benchmarks, including challenging DRONECROWD and VSCROWD.
DGS: Dual Gradient and Semantic-Shift Guided Low-Rank Adaptation for Class Incremental Learning
PDF ↗In Class-Incremental Learning (CIL), parameter efficient fine-tuning applied to Pre-trained Models (PTMs) remain vulnerable to catastrophic forgetting as they adapt to new tasks. The prevalent strategy to mitigate catastrophic forgetting is to constrain gradients within the orthogonal subspaces of past tasks, while rigid gradient constraints hinder plasticity. In this paper, we propose a novel CIL framework, Dual Gradient and Semantic-Shift Guided Low-Rank Adaptation (DGS), that balances stability and plasticity via gradient fusion and maintains representation consistency through classifier and patch-token alignment. Specifically, our method introduces the Dual Gradient update strategy that first derives a base subspace projection from the PTMs and then fuses task-specific LoRA gradients with their aligned counterparts through interpolated combination. This design promotes knowledge retention without sacrificing task-specific expressiveness. Furthermore, we employ a Classifier Alignment mechanism with Semantic shift estimation which is based on the calibrated prototype statistics to mitigate classifier shift, and introduce a novel Patch-level Alignment loss to preserve feature consistency across tasks. Extensive experiments on six standard benchmarks demonstrate that our approach consistently outperforms existing CIL methods, highlighting its effectiveness and generalization capability in continual learning scenarios.
Model merging aims to combine multiple task-specific experts into a single model, but inter-task interference often causes severe degradation, especially when the experts are trained on heterogeneous objectives. Existing data-free methods are practical, yet largely rely on parameter-space heuristics without explicitly modeling task statistics. In this paper, we show that under a local linear approximation, the input covariance of each task can be estimated directly from its fine-tuning update, providing a principled bridge between parameter changes and data geometry in the data-free setting. Based on this insight, we propose ACE-Merging, a closed-form model merging framework with adaptive covariance normalization, a collective structural prior, and spectral refinement. Across both vision and language benchmarks, ACE-Merging achieves strong overall performance among data-free baselines. In particular, it improves the average score on GPT-2 by more than 4 points over prior methods, while also delivering strong scalability and competitive efficiency. Our code is available at https://github.com/unravel-xu/ACE-Merging/tree/main.
IrisFP: Adversarial-Example-based Model Fingerprinting with Enhanced Uniqueness and Robustness
PDF ↗We propose IrisFP, a novel adversarial-example-based model fingerprinting framework that enhances both uniqueness and robustness by leveraging multi-boundary characteristics, multi-sample behaviors, and fingerprint discriminative power assessment to generate composite-sample fingerprints. Three key innovations make IrisFP outstanding: 1) It positions fingerprints near the intersection of all decision boundaries--unlike prior methods that target a single boundary--thus increasing the prediction margin without placing fingerprints deep inside target class regions, enhancing both robustness and uniqueness; 2) It constructs composite-sample fingerprints, each comprising multiple samples close to the multi-boundary intersection, to exploit collective behavior patterns and further boost uniqueness; and 3) It assesses the discriminative power of generated fingerprints using statistical separability metrics developed based on two reference model sets respectively for pirated and independently-trained models, and assigns fingerprint-specific thresholds to retained fingerprints. Extensive experiments show that IrisFP consistently outperforms state-of-the-art methods, achieving reliable ownership verification by enhancing both robustness and uniqueness.
Density ratio estimation is a core concept in statistical machine learning because it provides a unified mechanism for tasks such as importance weighting, divergence estimation, and likelihood-free inference, but its potential in vision and language models has not been fully explored. Modern vision-language encoders such as CLIP and SigLIP are trained with contrastive objectives that implicitly optimize log density ratios between joint and marginal image-text distributions, which implicitly learn similarity scores proportional to log density ratios. However, prior work has largely focused on their embedding utility, and the density-ratio structure induced by contrastive learning has not been systematically examined or exploited in multimodal applications. To address this gap, we reinterpret CLIP-style models as pretrained and general-purpose density ratio estimators and show that this perspective enables new algorithmic capabilities. We present a unified explanation of how contrastive objectives estimate density ratios and propose two practical applications: Importance Weight Learning and KL divergence estimation. Our Importance Weight Learning method requires only a single additional prompt and improves F1 scores by up to 7 points. We further show that CLIP-based density ratios support estimation of KL divergences that quantify how conditioning on an image or text alters the distribution of the other modality. Through qualitative examples and an N-gram analysis of captions, we find that these divergences capture semantic diversity and mode structure in multimodal data. Leveraging this property, we introduce a simple KL-guided data curation method that achieves performance competitive with LAION2B filtering. Our code is available at https://github.com/fumiyauchiyama/CLIP_Density_Ratio .
Measure The Feature Universe: Topology-based Pseudo Labeling and Gravity Consistency for Source-Free Domain Adaptation
PDF ↗Source-free domain adaptation (SFDA) adapts a pre-trained source model to an unlabeled target domain using only the model itself, typically relying on pseudo labeling augmented with auxiliary knowledge and consistency regularization (CR) mechanisms to alleviate noise in the generated pseudo labels. However, existing approaches overlook the geometric structure of the target embedding manifold when assigning pseudo labels, resulting in unreliable distance measurements and consequently severe mislabeling. Moreover, existing CR is applied solely to output logits, making it insensitive to feature-level reliability. To solve these issues, we propose a novel pseudo labeling scheme based on Feature universe, which is an expanded embedding space that models class-wise target distributions and Gravity consistency (GV) regularization, which modulates consistency strength according to feature-level similarity. Our pseudo labeling strategy first models the embedding space with virtual features to construct a feature universe. On this space, pseudo labels are generated through feature traversal, which propagates labels only from statistically reliable regions. In addition, GV jointly encourages logit- and feature-level consistency, aligning predictions for augmented images while preserving the geometric structure of the embedding space. It further modulates the strength of CR for each sample, preventing the confirmation of noisy pseudo labels through a gravity-based force defined between two input embeddings. Experiments on Office-Home, DomainNet-126, and VisDA-C demonstrate consistent improvements over prior SFDA methods, and incorporating GV into baselines yields additional gains.
Consistent 3D geometry estimation from streaming RGB input is crucial for real-world applications such as autonomous driving, embodied AI, and large-scale reconstruction. While modern monocular geometry foundation models achieve strong single-image accuracy, they exhibit severe temporal inconsistency on continuous input, notably dominated by scale-shift drifting. Through targeted empirical analysis, we trace this instability to its root cause: fluctuations in latent feature statistics, whose mean and variance directly determine the predicted depth's scale and shift. Building on this insight, we introduce Dynamic Feature Normalization (DyFN), a lightweight, causal recurrent module that dynamically and robustly modulates feature statistics to maintain stable geometry over time. We adapt powerful pretrained monocular geometry models for streaming by finetuning only DyFN, a mere 2% additional parameters, while keeping the backbone frozen, thereby achieving temporal consistency without compromising single-image accuracy. Extensive experiments across four benchmarks show that DyFN effectively eliminates temporal artifacts such as disjointed layering and positional jitter, and achieves state-of-the-art temporal stability, improving over prior streaming methods by up to 14% and even outperforming heavier non-causal video baselines. Project page: https://shawlyu.github.io/DyFN
ELiC: Efficient LiDAR Geometry Compression via Cross-Bit-depth Feature Propagation and Bag-of-Encoders
PDF ↗Hierarchical LiDAR geometry compression encodes voxel occupancies from low to high bit-depths, yet prior methods treat each depth independently and re-estimate local context from coordinates at every level, limiting compression efficiency. We present ELiC, a real-time framework that combines cross-bit-depth feature propagation, a Bag-of-Encoders (BoE) selection scheme, and a Morton-order-preserving hierarchy. Cross-bit-depth propagation reuses features extracted at denser, lower depths to support prediction at sparser, higher depths. BoE selects, per depth, the most suitable coding network from a small pool, adapting capacity to observed occupancy statistics without training a separate model for each level. The Morton hierarchy maintains global Z-order across depth transitions, eliminating per-level sorting and reducing latency. Together these components improve entropy modeling and computation efficiency, yielding state-of-the-art compression at real-time throughput on Ford and SemanticKITTI. Code and pretrained models are available at http://github.com/moolgom/ELiCv1.
Cov2Pose: Leveraging Spatial Covariance for Direct Manifold-aware 6-DoF Object Pose Estimation
PDF ↗In this paper, we address the problem of 6-DoF object pose estimation from a single RGB image. Indirect methods that typically predict intermediate 2D keypoints, followed by a Perspective-n-Point solver, have shown great performance. Direct approaches, which regress the pose in an end-to-end manner, are usually computationally more efficient but less accurate. However, direct pose regression heads rely on globally pooled features, ignoring spatial second-order statistics despite their informativeness in pose prediction. They also predict, in most cases, discontinuous pose representations that lack robustness. Herein, we therefore propose a covariance-pooled representation that encodes convolutional feature distributions as a symmetric positive definite (SPD) matrix. Moreover, we propose a novel pose encoding in the form of an SPD matrix via its Cholesky decomposition. Pose is then regressed in an end-to-end manner with a manifold-aware network head, taking into account the Riemannian geometry of SPD matrices. Experiments and ablations consistently demonstrate the relevance of second-order pooling and continuous representations for direct pose regression, including under partial occlusion.
Test-Time Adaptation (TTA) aims to mitigate distributional shifts between training and test domains during inference time. However, existing TTA methods fall short in the realistic scenario where models face both continually changing domains and the simultaneous emergence of unknown semantic classes --- a challenging setting we term Open-set Continual Test-Time Adaptation (OCTTA). The coupling of domain and semantic shifts often collapses the feature space, severely degrading both classification and out-of-distribution detection. To tackle this, we propose DOmain COmpensation (DOCO), a lightweight and effective framework that robustly performs domain adaptation and OOD detection in a synergistic, closed loop. DOCO first performs dynamic, adaptation-conditioned sample splitting to separate likely ID from OOD samples. Then, using only the ID samples, it learns a domain compensation prompt by aligning feature statistics with the source domain, guided by a structural preservation regularizer that prevents semantic distortion. This learned prompt is then propagated to the OOD samples within the same batch, effectively isolating their semantic novelty for more reliable detection. Extensive experiments on multiple challenging benchmarks demonstrate that DOCO outperforms prior CTTA and OSTTA methods, establishing a new state-of-the-art for the demanding OCTTA setting.