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首次观测 · 记录 #1CVF · CVPR 2026:cvf:ab8bf3d8caf6dec6bc52050f1c9d41fcCVF · CVPR 2026

Render-to-Adapt: Unsupervised Personal Adaptation for Gaze Estimation

Ge, Yangshi, Liu, Zheng, Lu, Feng

原文摘要

Deep learning-based gaze estimation methods tend to suffer from substantial performance drop in real-world scenarios with varying users and environments. To tackle this issue, most recent approaches employ Unsupervised Domain Adaptation (UDA) to bridge the gap between source and target domains. However, this paradigm is misaligned with real-world scenarios, where the system typically needs to adapt to only a single new user. Therefore, this paper advocates a more practical paradigm: Unsupervised Personal Adaptation (UPA), which calibrates a pre-trained model using a few unlabeled images from a single new user. Conventional UDA methods do not guarantee improvements for every user and often yield lower average performance in this setting. To address this problem, we propose Render-to-Adapt (R2A), a self-supervised framework specifically designed for the UPA task. Given a pretrained gaze model, R2A utilizes a gaze-conditioned renderer to synthesize new images based on the model's gaze predictions, and enforces eye-region consistency as a label-free signal to enhance personalized gaze estimation. We evaluate R2A on a re-designed cross-dataset personal adaptation benchmark. Experimental results show that R2A consistently improves performance across all individuals and significantly outperforms existing SOTA methods.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:b61bf10d23e0c239a2bc961678269d4aCVF · CVPR 2026

SAME: Sparse and Anchored Model Editing for Heterogeneous Incremental Learning under Limited Data

Duan, Zixuan, Zhang, Zeyu, Lu, Fengyuan, Zhang, Shaofeng, Li, Wenbin, Fan, Qi, Gao, Yang

原文摘要

Existing Incremental Learning (IL) methods are primarily evaluated under either a single-domain class-incremental setting, or a multi-domain task-incremental setting with known task identifiers. However, these assumptions often fail to hold in real-world applications. To bridge this gap, we introduce Heterogeneous Incremental Learning (HIL), a new setting for evaluating IL methods under realistic and challenging conditions, where task boundaries are ambiguous or unknown, class distributions shift dynamically across environments, and training data is limited. Model editing is inherently well-suited for this challenging HIL, as it allows for the efficient integration of new knowledge while preserving model capabilities. Thus, we propose a novel Sparse and Anchored Model Editing (SAME) for addressing HIL. Specifically, SAME sparsely and selectively updates task-relevant model parameters to extract compact, task-specific key-value knowledge pairs from limited data. Using these task knowledge pairs, the model performs knowledge injection for new tasks under double-anchor constraints. The knowledge anchor aligns the updated and original model features, while the parameter anchor constrains parameter magnitudes, ensuring stable and consistent knowledge injection. Our method can efficiently solve HIL using only a few labeled examples, without introducing additional model parameters. Extensive experiments on 11 diverse visual-language datasets across 22 sequential tasks show that our method outperforms existing continual learning approaches by 6.8% in average accuracy, while retaining 95.8% of the oracle model performance, demonstrating strong stability and cross-domain generalization.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:df9b7feb0e84f80c080b9d7fd1e6dbf7CVF · CVPR 2026

GazeOnce360: Fisheye-Based 360deg Multi-Person Gaze Estimation with Global-Local Feature Fusion

Cai, Zhuojiang, Sun, Zhenghui, Lu, Feng

原文摘要

We present GazeOnce360, a novel end-to-end model for multi-person gaze estimation from a single tabletop-mounted upward-facing fisheye camera. Unlike conventional approaches that rely on forward-facing cameras in constrained viewpoints, we address the underexplored setting of estimating the 3D gaze direction of multiple people distributed across a 360deg scene from an upward fisheye perspective. To support research in this setting, we introduce MPSGaze360, a large-scale synthetic dataset rendered using Unreal Engine, featuring diverse multi-person configurations with accurate 3D gaze and eye landmark annotations. Our model tackles the severe distortion and perspective variation inherent in fisheye imagery by incorporating rotational convolutions and eye landmark supervision. To better capture fine-grained eye features crucial for gaze estimation, we propose a dual-resolution architecture that fuses global low-resolution context with high-resolution local eye regions. Experimental results demonstrate the effectiveness of each component in our model. This work highlights the feasibility and potential of fisheye-based 360deg gaze estimation in practical multi-person scenarios. Project page: https://caizhuojiang.github.io/GazeOnce360/.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:ed303b3117aa2f62e6be69dc8e468471CVF · CVPR 2026

Through the Frequency Lens: Cross-Domain Generalisable Gaze Estimation with Adaptive Modulation

Xu, Yang, Bao, Yiwei, Lu, Feng

原文摘要

Deep learning-based gaze estimation methods often exhibit significant performance degradation on unseen target domains. Through systematic frequency-domain analysis, we reveal that face images contain frequency components with distinct contributions: some facilitate cross-domain generalization while others introduce domain-specific interference that impedes it, with both components varying across datasets and constituting a key source of domain gap. Based on these observations, we propose the Frequency-Guided Adaptive Learning framework (FGAL), a novel framework enhancing domain generalization without accessing target domain data. The FGAL consists of two complementary modules: the Adaptive Interference Suppression Module (AISM) and the Spectrum Diversification Module (SDM). AISM adaptively suppresses sample-specific interfering frequency components through learnable modulation maps, while SDM diversifies frequency distribution patterns to enhance robustness against cross-domain variations. Experiments demonstrate that FGAL achieves substantial improvements, outperforming baselines by up to 28.2% and state-of-the-art methods by up to 19.5% across multiple cross-domain settings, demonstrating our framework's potential for broader domain generalization tasks.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗