In the online metric matching problem, $n$ servers and $n$ requests lie in a metric space. Servers are available upfront, and requests arrive sequentially. An arriving request must be matched immediately and irrevocably to an available server, incurring a cost equal to their distance. The goal is to minimize the total matching cost. We study this problem in the Euclidean metric $[0, 1]^d$, when servers are adversarial and requests are independently drawn from distinct distributions that satisfy a mild smoothness condition. Our main result is an $O(1)$-competitive algorithm for $d \neq 2$ that requires no distributional knowledge, relying only on a single sample from each request distribution. To our knowledge, this is the first algorithm to achieve an $o(\log n)$ competitive ratio for non-trivial metrics beyond the i.i.d. setting. Our approach bypasses the $Ω(\log n)$ barrier introduced by probabilistic metric embeddings: instead of analyzing the embedding distortion and the algorithm separately, we directly bound the cost of the algorithm on the target metric of a simple deterministic embedding. We then combine this analysis with lower bounds on the offline optimum for Euclidean metrics, derived via majorization arguments, to obtain our guarantees.

Data-driven algorithm selection is a powerful approach for choosing effective heuristics for computational problems. It operates by evaluating a set of candidate algorithms on a collection of representative training instances and selecting the one with the best empirical performance. However, running each algorithm on every training instance is computationally expensive, making scalability a central challenge. In practice, a common workaround is to evaluate algorithms on smaller proxy instances derived from the original inputs. However, this practice has remained largely ad hoc and lacked theoretical grounding. We provide the first theoretical foundations for this practice by formalizing the notion of size generalization: predicting an algorithm's performance on a large instance by evaluating it on a smaller, representative instance, subsampled from the original instance. We provide size generalization guarantees for three widely used clustering algorithms (single-linkage, k-means++, and Gonzalez's k-centers heuristic) and two canonical max-cut algorithms (Goemans-Williamson and Greedy). We characterize the subsample size sufficient to ensure that performance on the subsample reflects performance on the full instance, and our experiments support these findings.

Eye tracking technology, essential for enhancing user experience in virtual reality (VR) and augmented reality (AR) devices, has been widely incorporated into advanced head-mounted devices like the Apple Vision Pro and PICO 4 Pro, becoming a standard feature. However, dedicated eye tracking datasets for such devices are severely lacking, with existing datasets commonly facing issues like camera skew and low resolution, particularly failing to adequately consider the diversity in wearing postures. To address this gap, we have developed the Posture-Variant Eye Tracking Dataset (PVEye), which includes 11,044,800 high-resolution near-eye images from 104 participants, showcasing a rich variety of wearing postures. This dataset aims to advance the development and application of appearance-based eye tracking methods. Utilizing this dataset, our evaluations demonstrate that the appearance-based method, particularly the NVGaze model, provides improved accuracy and robustness compared to the traditional feature-based method. Crucially, our experiments indicate that variations in wearing posture can significantly impact eye tracking performance, with posture-related errors contributing approximately 45% to the overall error variance. Moreover, the study delves into the specific impact of calibration and other critical factors on eye tracking performance, offering insights for further optimization of tracking effectiveness.

Iris recognition has gained significant attention in identity verification due to the unique, stable texture patterns in iris. Successfully extracting these patterns is essential for quick and precise identification. Although deep learning methods have automated the iris recognition, they predominantly rely on real-valued networks that overlook the complex-valued representation of iris texture. This means they cannot effectively process phase and amplitude information, and fail to integrate domain-specific knowledge of iris, thereby not fully capturing the intricate details of the iris texture. Inspired by classical manual methods that efficiently harness the complex-valued representation of the iris to extract both amplitude and phase information. We integrate Gabor filters with complex-valued neural networks, propose a Complex-Valued Gabor-Attention Residual Fusion Network (GRFN) tailored for iris recognition, aiming to comprehensively capture the iris texture’s multi-scale and multi-orientation phase and amplitude features. The GRFN incorporates adaptive Gabor Complex-Valued Convolution Kernels (GCVK) to introduce a Gabor attention mechanism focused on iris biometric characteristics. Furthermore, we propose a novel residual feature fusion approach that selects and merges local and global features across multiple directions and scales, mitigating model degradation and enhancing the network’s ability to extract iris texture features effectively. Extensive experiments show that the proposed network outperforms the state-of-the-art performance on two benchmark datasets.

Gaze is a crucial element in human-computer interaction and plays an increasingly vital role in promoting the adoption of head-mounted devices (HMDs). Existing gaze tracking methods for HMDs either demand user calibration or face challenges in balancing accuracy and speed, compromising the overall user experience. In this paper, we introduce a novel strategy for real-time, calibration-free gaze tracking using joint head-eye cues on HMDs. Initially, we create a multimodal gaze tracking dataset named HE-Gaze, encompassing synchronized eye images and 6DoF head movement data, addressing a gap in the current data landscape. Statistical analyses unveil the correlation between head movements and gaze positions. Building on these insights, we introduce the hierarchical head-eye coordinated gaze tracking model (HHE-Tracker), which incorporates two lightweight branches to encode input eye images and head sequences efficiently. It combines encoded head velocity and posture features with eye features across various scales to infer gaze position. HHE-Tracker was implemented on a commercial HMD, and its performance was assessed in unconstrained scenarios. The results demonstrate the HHE-Tracker's capability to accurately estimate gaze positions in real-time. In comparison to the state-of-the-art gaze tracking algorithm, HHE-Tracker exhibits commendable accuracy (3.47$^{\circ }$) and a 40-fold speedup (81FPSon a Snapdragon 845 SoC).

High-accuracy, low-latency gaze tracking is becoming one of the indispensable features in augmented reality (AR) head-mounted devices (HMDs). Researchers have proposed different approaches to predict gaze positions from eye images. However, since only the eye modality is focused, these appearance-based algorithms are still struggle to trade off the accuracy and running speed in HMDs. In this paper, we propose a lightweight multi-modal network (HE-Tracker) to regress gaze positions. By fusing head-movement features with eye features, HE-Tracker achieves comparable accuracy (3.655° in all subjects) and $27 \times$ speedup (48 fps in the specialized AR HMD) compared to the state-of-the-art gaze tracking algorithm. We further demonstrate that when applying our head-eye coordination strategy to other baseline models, all these models achieve at least 6.36% performance improvement without a pronounced effect on running speed. Moreover, we construct HE-Gaze, the first multi-modal dataset with eye images and head-movement data for near-eye gaze tracking. This dataset is currently made of 757,360 frames and 15 persons, providing an opportunity to foster research in multi-modal gaze tracking approaches. Our dataset is available at DOWNLOAD LINK1.