Out-of-distribution (OOD) detection aims to identify samples that deviate from in-distribution (ID). One popular pipeline addresses this by introducing negative labels distant from ID classes and detecting OOD based on their distance to these labels.However, such labels may present poor activation on OOD samples, failing to capture the OOD characteristics. To address this, we propose \underline T est-time \underline A ctivated \underline N egative \underline L abels (TANL) by dynamically evaluating activation levels across the corpus dataset and mining candidate labels with high activation responses in the testing process. Specifically, TANL identifies high-confidence test images online and accumulates their assignment probabilities over the corpus to construct a label activation metric.Such a metric leverages historical test samples to adaptively align with the test distribution, enabling the selection of distribution-adaptive activated negative labels. By further exploring the activation information within the current testing batch, we introduce a more fine-grained, batch-adaptive variant. To fully utilize label activation knowledge, we propose an activation-aware score function that emphasizes negative labels with stronger activations, boosting performance and enhancing its robustness to the label number.Our TANL is training-free, test-efficient, and grounded in theoretical justification. Experiments on diverse backbones and wide task settings validate its effectiveness.Notably, on the large-scale ImageNet benchmark, TANL significantly reduces the FPR95 from 17.5% to 9.8%.Codes will be released.
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cs.CV · cs.CL
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
3D Gaussian Splatting (3DGS) has demonstrated exceptional performance in reconstruction and novel view synthesis tasks. However, its reliance on Structure-from-Motion preprocessing may lead to degraded performance under sparse-view scenarios. Recent works attempt to address this limitation by leveraging pre-trained image matching models to generate Gaussian primitives but overlook the probabilistic uncertainty embedded in both the initial primitive distribution and iterative position updates. This uncertainty can accumulate and degrade reconstruction fidelity. Hence, we propose BA-GS, a Bayesian framework that models both the global distribution and local uncertainty of Gaussian primitives. At global initialization, a Variational Bayesian Gaussian Mixture Model (VB-GMM) models the latent distribution of primitives, capturing region-wise density and gradient patterns. At local refinement, an Adaptive Kalman Filter refines each primitive's position by recursively fusing noisy gradient observations with spatial priors, dynamically adjusting its covariance according to local uncertainty.This hierarchical Bayesian formulation effectively bridges probabilistic distribution modeling and uncertainty-aware optimization, resulting in improved reconstruction quality under sparse-view conditions. Experiments across multiple benchmark datasets including Tanks and Temples, MVimgNet, and LLFF demonstrate that our method consistently outperforms existing approaches.
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cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
Common image editing tasks typically adopt powerful generative diffusion models as the leading paradigm for real-world content editing. Meanwhile, although reinforcement learning (RL) methods such as Diffusion-DPO and Flow-GRPO have further improved generation quality, efficiently applying Reinforcement Learning from Human Feedback (RLHF) to diffusion-based editing remains largely unexplored, due to a lack of scalable human-preference datasets and frameworks tailored to diverse editing needs. To fill this gap, we propose HP-Edit, a post-training framework for Human Preference-aligned Editing, and introduce RealPref-50K, a real-world dataset across eight common tasks and balancing common object editing. Specifically, HP-Edit leverages a small amount of human-preference scoring data and a pretrained visual large language model (VLM) to develop HP-Scorer--an automatic, human preference-aligned evaluator. We then use HP-Scorer both to efficiently build a scalable preference dataset and to serve as the reward function for post-training the editing model. We also introduce RealPref-Bench, a benchmark for evaluating real-world editing performance. Extensive experiments demonstrate that our approach significantly enhances models such as Qwen-Image-Edit-2509, aligning their outputs more closely with human preference.
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cs.CV · cs.CL
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
Multimodal Large Language Models (MLLMs) are undergoing rapid progress and represent the frontier of AI development. However, their training and inference efficiency have emerged as a core bottleneck in making MLLMs more accessible and scalable. To address the challenges, we present MiniCPM-V 4.5, an 8B parameter model designed for high efficiency and strong performance. We introduce three core improvements in model architecture, data strategy and training method: a unified 3D-Resampler model architecture for highly compact encoding over images and videos, a unified learning paradigm for document knowledge and text recognition without heavy data engineering, and a hybrid reinforcement learning strategy for proficiency in both short and long reasoning modes. Comprehensive experimental results in OpenCompass evaluation show that MiniCPM-V 4.5 surpasses widely used proprietary models such as GPT-4o-latest, and significantly larger open-source models such as Qwen2.5-VL 72B. Notably, the strong performance is achieved with remarkable efficiency. For example, on the widely adopted VideoMME benchmark, MiniCPM-V 4.5 surpasses Qwen2.5-VL 7B by +2.8, using just 46.7% GPU memory and 8.7% inference time.
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cs.CV · cs.CL
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
High-quality video editing and processing are crucial in domains such as filmmaking and autonomous driving, where accurate visual refinement and data preparation are essential. However, it is challenging to achieve precise control over dynamic objects while maintaining spatiotemporal consistency. Current approaches typically utilize text prompts or 2D structural priors for video editing to ensure consistency, yet they struggle to effectively constrain the spatial variations of dynamic 3D objects. In this paper, we introduce RecEdit-Drive, a framework that integrates Spatial Feature Warping and Spatiotemporal Collaborative Modeling to effectively control 3D object variations and enhance video consistency. The spatial feature warping enhances precise control over the edited foreground 3D objects, enhancing spatial consistency in the generated videos; and the spatiotemporal collaborative modeling seamlessly integrates edited foreground objects into the background, yielding realistic and consistent edited videos. Besides, we design an inference strategy to reconstruct an accurate background structure through noise manipulation, providing a reliable reference for foreground instance editing at early denoising stages. We perform extensive qualitative and quantitative comparisons regarding general video editing and downstream tasks on the public datasets, demonstrating the state-of-the-art performance of our proposed method.
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cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
Neural fields (NFs) have achieved remarkable success in scene reconstruction and novel view synthesis. However, existing NF approaches that rely on RGB or LiDAR inputs often struggle under adverse weather conditions, limiting their robustness in real-world outdoor environments such as autonomous driving. In contrast, millimeter-wave radar is inherently resilient to environmental variations, yet its integration with NFs remains largely underexplored. Moreover, outdoor driving scenes frequently involve dynamic objects, making spatiotemporal modeling crucial for temporally consistent novel view synthesis. To address these challenges, we present RF4D, a radar-based neural field framework tailored for novel view synthesis in outdoor dynamic scenes. RF4D explicitly incorporates temporal information into its representation, enabling more accurate modeling of object motion. A dedicated scene flow module further predicts temporal offsets between adjacent frames, enforcing temporal occupancy coherence during dynamic scene reconstruction. Moreover, we propose a radar-specific power rendering formulation grounded in radar sensing physics, improving both synthesis accuracy and interpretability. Extensive experiments on public radar datasets demonstrate that RF4D substantially outperforms existing methods in radar measurement synthesis and occupancy estimation accuracy, with particularly strong gains in dynamic outdoor environments.
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cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
Based on an RGB-D image pair, semantic scene completion (SSC) provides a description for 3D scene understanding by predicting 3D semantic occupancy map. Recent methods extract RGB-D multi-modal features and fuse them in spatial domain, which disregards the misalignment caused by the imperfect raw multi-modal data and the multi-modal feature learning. Moreover, the operations of extracting high-level features they utilized tend to introduce feature smoothing and detail loss, exacerbating the above misalignment. To tackle these problems, this paper introduces MFDNet, a lightweight semantic scene completion network based on a multi-modal frequency decomposition strategy. By integrating frequency processing with limited layers of convolution and downsampling, MFDNet achieves a balance between modalities alignment and detail retainment. The network is equipped with Multi-modal Adaptive Frequency Fusion (MAFF) and Frequency Detail Compensation (FDC). MAFF models the intra-modal multi-bands dependencies and inter-modal relationships from a global perspective, enabling modality-specific calibration while facilitating the aligned fusion of multi-modal features. FDC excavates the high-frequency cues in shallow features to compensate for the missing local details of the fused feature and achieve fine-grained alignment for completion. MAFF and FDC formulate a global-to-local alignment and completion paradigm for multi-modal SSC. Extensive experiments demonstrate that MFDNet reduces parameters by 54.4% while achieving state-of-the-art performance on the NYUv2 and NYUCAD datasets.
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cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
Instruction-based multimodal image manipulation has recently made rapid progress. However, existing evaluation methods lack a systematic and human-aligned framework for assessing model performance on complex and creative editing tasks. To address this gap, we propose CREval, a fully automated question-answer (QA)-based evaluation pipeline that overcomes the incompleteness and poor interpretability of opaque Multimodal Large Language Models (MLLMs) scoring. Simultaneously, we introduce CREval-Bench, a comprehensive benchmark specifically designed for creative image manipulation under complex instructions. CREval-Bench covers three categories and nine creative dimensions, comprising over 800 editing samples and 13K evaluation queries. Leveraging this pipeline and benchmark, we systematically evaluate a diverse set of state-of-the-art open and closed-source models. The results reveal that while closed-source models generally outperform open-source ones on complex and creative tasks, all models still struggle to complete such edits effectively. In addition, user studies demonstrate strong consistency between CREval's automated metrics and human judgments. Therefore, CREval provides a reliable foundation for evaluating image editing models on complex and creative image manipulation tasks, and highlights key challenges and opportunities for future research.
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cs.CV · cs.CL
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
Foundation models have transformed vision and language by learning general-purpose representations from large-scale unlabeled data, yet 3D medical imaging lacks analogous approaches. Existing self-supervised methods rely on low-level reconstruction or contrastive objectives that fail to capture the anatomical semantics critical for medical image analysis, limiting transfer to downstream tasks. We present MASS (MAsk-guided Self-Supervised learning), which treats in-context segmentation as the pretext task for learning general-purpose medical imaging representations. MASS's key insight is that automatically generated class-agnostic masks provide sufficient structural supervision for learning semantically rich representations. By training on thousands of diverse mask proposals spanning anatomical structures and pathological findings, MASS learns what semantically defines medical structures: the holistic combination of appearance, shape, spatial context, and anatomical relationships. We demonstrate effectiveness across data regimes: from small-scale pretraining on individual datasets (20-200 scans) to large-scale multi-modal pretraining on 5K CT, MRI, and PET volumes, all without annotations. MASS demonstrates: (i) few-shot segmentation on novel structures, (ii) matching full supervision with only 20-40% labeled data while outperforming self-supervised baselines by over 20 in Dice score in low-data regimes, and (iii) frozen-encoder classification on unseen pathologies that matches full supervised training with thousands of samples. Mask-guided self-supervised pretraining captures broadly generalizable knowledge, opening a path toward 3D medical imaging foundation models without expert annotations. Code is available \href https://github.com/Stanford-AIMI/MASS here .
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cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表