Domain generalization in person re-identification (DG re-ID) aims to build models that can accurately retrieve a targeted person across cameras in arbitrary unseen domains, without access to those domains during training. Data augmentation (DA) has become a de facto solution to DG re-ID, and one line of approaches realizes this goal from a frequency-centric perspective, where the Fourier Transform (FT) is employed to mix amplitudes for novel sample generation. However, FT-based strategies are fundamentally limited by the assumption of signal stationarity, which may be inadequate to explore a broader perturbation space. To overcome this limitation, this work investigates the potential of the Fractional Fourier Transform (FrFT) for DA and proposes FracMix (Fractional Fourier based MixUp), which yields an intermediate representation that bridges spatial and frequency domains with a fractional order α, enabling the generation of richer samples. Furthermore, an Adaptive Token Pruning strategy (ATP) is designed to dynamically select the top-K salient tokens and perform perturbations exclusively to corresponding patches. This simple yet effective mechanism alleviates over-reliance on a small set of dominant patches and encourages broader contextual utilization, thereby improving robustness and generalization. Experiments on multiple DG re-ID benchmarks demonstrate the effectiveness of FracMix.

Microscopic 3D shape reconstruction using depth from focus (DFF) is crucial in precision manufacturing for 3D modeling and quality control. However, the absence of high-precision microscopic DFF datasets and the significant differences between existing DFF datasets and microscopic DFF data in optical design, imaging principles and scene characteristics hinder the performance of current DFF models in microscopic tasks. To address this, we introduce M3D, a novel microscopic DFF dataset, constructed using a self-developed microscopic device. It includes multi-focus image sequences of 1,952 scenes across five categories, with depth labels obtained through the 3D TFT algorithm applied to dense image sequences for initial depth estimation and calibration. All labels are then compared and analyzed against the design values, and those with large errors are eliminated. We also propose M3DNet, a frequency-aware end-to-end network, to tackle challenges like shallow depth-of-field (DoF) and weak textures. Results show that M3D compensates for the limitations of macroscopic DFF datasets and extends DFF applications to microscopic scenarios. M3DNet effectively captures rapid focus decay and improves performance on public DFF datasets by leveraging superior global feature extraction. Additionally, it exhibits strong robustness even in extreme conditions. Dataset and code are available at https://github.com/jiangfeng-Z/M3D.

Person re-identification (re-ID) models often fail to generalize well when deployed to unseen camera networks with domain shift. Domain generalization (DG) aims to address this dilemma by training a model on source domains that learns domain-invariant, and hence generalizable representations. Most methods in the literature are based on Convolutional Neural Networks (CNNs), while the DG performance of Vision Transformers (ViTs) remains relatively unexplored. In contrast to CNNs, ViTs lack explicit inductive biases, which makes it extremely data-hungry and easily overfit to source domains. In this paper, we investigate the generalization ability of ViTs and propose a novel Siamese and Self Distillation Vision Transformer (SSDViT) framework towards addressing the DG re-ID problem. To be specific, Siamese Distillation exploits the weak-to-strong consistency regularization at the image level to enforce the strongly perturbed image to yield consistent prediction with its weakly perturbed version, which provides a simple yet effective solution to introduce invariance bias. On the other hand, Self Distillation seeks to impose consistency constraints at the feature level by leveraging intermediate knowledge to improve the robustness of learned representations. The proposed unified framework pursues the equivalence of predictions at both the image and embedding levels, which underpins the generalization capabilities of learned representations and alleviates the risk of overfitting to source domains. Without bells and whistles, the proposed approach achieves a new state-of-the-art on various DG re-ID benchmarks. Codes are available at https://github.com/yJCTrans/SSDViT.

In large aperture imaging, the shallow depth of field (DoF) phenomenon requires capturing multiple images at different focal levels, allowing us to infer depth information using depth from focus (DFF) techniques. However, most previous works design convolutional neural networks from a time domain perspective, often leading to blurred fine details in depth estimation. In this work, we propose a frequency-aware deep DFF network (FAD) that couples multi-scale spatial domain local features with frequency domain global structural features. Our main innovations include two key points: First, we introduce a frequency domain feature extraction module that uses the Fourier transform to transfer latent focus features into the frequency domain. This module adaptively captures essential frequency information for focus changes through element-wise multiplication, enhancing fine details in depth results while preserving global structural integrity. Second, the time-frequency joint module of FAD improves the consistency of depth information in sparse texture regions and the continuity in transition areas from both local and global complementary perspectives. Comprehensive experiments demonstrate that our model achieves compelling generalization and state-of-the-art depth prediction across various datasets. Additionally, it can be quickly adapted to real-world applications as a pre-trained model.