arXiv subjects

Tingwei Wang

Publications and source records attributed to Tingwei Wang.

3 recordsLinked to original sources

arXiv2026

Few-Shot Video Recognition via Hierarchical Metric Learning

Few-shot action recognition (FSAR) aims to recognize unseen action categories with only a small number of annotated video samples. Recent works typically apply single-prototype supervision at the network output and fail to sufficiently exploit rich cross-frame global spatial information in videos. Even existing multi-level metric schemes only impose parallel prototype constraints on intermediate layers, without progressive supervision along the full feature pipeline, which results in limited generalization ability of the learned class prototypes. Inspired by this, we present a novel method, hierarchical metric learning for few-shot action recognition (HML-FSAR). First, a spatial-enhanced module is developed to capture cross-frame global spatial representations. Combined with temporal MHA, heterogeneous alignment, spatial-temporal feature fusion and dictionary learning modules, it constructs the complete feature processing pipeline. Second, a hierarchical metric learning (HML) strategy is embedded into HML-FSAR. Composed of center metric, alignment metric, contrastive metric, dictionary metric and prototype metric, HML imposes progressive multi-stage complementary constraints from frame-level representations to final class prototypes, so as to jointly optimize feature compactness, heterogeneous spatial-temporal alignment, inter-class discriminability and anti-noise robustness. The proposed HML-FSAR method is validated on five widely-used FSAR datasets, and experimental results fully demonstrate its effectiveness.

cs.CV↗

arXiv2026

SA-LIVO: Efficient LiDAR-Inertial-Visual Odometry with Subspace-Aware Degeneracy Handling

Tightly coupled LiDAR-inertial-visual odometry (LIVO) fuses geometric depth with visual measurements, but its exteroceptive sensors fail independently: LiDAR when scan geometry is under-constrained, vision under poor illumination or texture absence. Existing countermeasures (binary degeneracy detection, covariance inflation, scene-level quality gating) act at the modality level, so a single isotropic gain sends visual residuals into directions LiDAR already constrains well and cannot concentrate them where constraints are deficient. We propose Subspace-Aware LiDAR-inertial-visual odometry (SA-LIVO), whose Subspace-Aware Information Fusion (SAIF) eigendecomposes the joint LiDAR-visual information matrix and gates each eigendirection by a single-threshold linear clamp, attenuating low-amplitude directions while passing well-observed ones at full strength; robust per-residual gating and a scene-level quality factor screen corrupted measurements. LiDAR and visual residuals share one invariant extended Kalman filter (InEKF) loop and linearization point, letting photometric Jacobians be assembled once and reused across iterations. On 29 public-benchmark sequences (HILTI'22, Newer College Dataset (NCD), Oxford Spires), plus additional concurrent-degradation scenarios, SA-LIVO matches the strongest baselines in accuracy and stays bounded where competing systems diverge. On the HILTI'22 subset that every baseline completes, it averages 12.3 ms per frame on a laptop CPU and 26.8 ms on an embedded ARM board without GPU, at 3.6-6.3x lower peak memory.

cs.RO↗

arXiv2022

Learning to Augment via Implicit Differentiation for Domain Generalization

Machine learning models are intrinsically vulnerable to domain shift between training and testing data, resulting in poor performance in novel domains. Domain generalization (DG) aims to overcome the problem by leveraging multiple source domains to learn a domain-generalizable model. In this paper, we propose a novel augmentation-based DG approach, dubbed AugLearn. Different from existing data augmentation methods, our AugLearn views a data augmentation module as hyper-parameters of a classification model and optimizes the module together with the model via meta-learning. Specifically, at each training step, AugLearn (i) divides source domains into a pseudo source and a pseudo target set, and (ii) trains the augmentation module in such a way that the augmented (synthetic) images can make the model generalize well on the pseudo target set. Moreover, to overcome the expensive second-order gradient computation during meta-learning, we formulate an efficient joint training algorithm, for both the augmentation module and the classification model, based on the implicit function theorem. With the flexibility of augmenting data in both time and frequency spaces, AugLearn shows effectiveness on three standard DG benchmarks, PACS, Office-Home and Digits-DG.

cs.CV↗