Gait recognition has attracted increasing attention in both academia and industry as a non-intrusive human recognition technology from a distance without requiring cooperation. Triplet loss, which enforces relative distance constraints, is a fundamental component in gait recognition. Recently, several gait-specific triplet losses have been introduced to gait recognition. However, they only focus on sample selection and weighting to enhance constraints without exploring the gradient properties of Cosine/Euclidean metrics, which fundamentally influence the model training efficiency and feature discriminability. In this paper, we theoretically analyze triplet loss gradients combined with weight decay and identify inherent limitations due to inadequate norm-control: Cosine metric triplet loss (Lcos) exhibits excessive gradients resulting from small feature norms, while Euclidean metric triplet loss (Leuc) suffers from a small margin-to-norm ratio due to large feature norms. To address these issues, we propose two norm-control approaches to constrain the feature norm in a stable range: 1) Norm-Variance-Regularized Collaboration. 2) Norm-Based Regularization. Extensive experiments show that our methods outperform state-of-the-art results under both Cosine and Euclidean evaluation metrics on three in-the-wild datasets: Gait3D, GREW, and BUAA-Duke-Gait. The code will be available at https://github.com/bgdpgz/TL-Gait.

Post-training quantization (PTQ) has stood out as a cost-effective and promising model compression paradigm in recent years, as it avoids computationally intensive model retraining. Nevertheless, current PTQ methods for Vision Transformers (ViTs) still suffer from significant accuracy degradation, especially under low-bit quantization. To address these shortcomings, we analyze the prevailing Hessian-guided quantization loss, and uncover certain limitations of conventional Hessian approximations. By following the block-wise reconstruction framework, we propose a novel PTQ method for ViTs, dubbed FIMA-Q. Specifically, we firstly establish the connection between KL divergence and FIM, which enables fast computation of the quantization loss during reconstruction. We further propose an efficient FIM approximation method, namely DPLR-FIM, by employing the diagonal plus low-rank principle, and formulate the ultimate quantization loss. Our extensive experiments, conducted across various vision tasks with representative ViT-based architectures on public datasets, demonstrate that our method substantially promotes the accuracy compared to the state-of-the-art approaches, especially in the case of low-bit quantization. The source code is available at https://github.com/ShiheWang/FIMA-Q.

Vision Transformers (ViTs) have become one of the most commonly used backbones for vision tasks. Despite their remarkable performance, they often suffer significant accuracy drops when quantized for practical deployment, particularly by post-training quantization (PTQ) under ultra-low bits. Recently, reconstruction-based PTQ methods have shown promising performance in quantizing Convolutional Neural Networks (CNNs). However, they fail when applied to ViTs, primarily due to the inaccurate estimation of output importance and the substantial accuracy degradation in quantizing post-GELU activations. To address these issues, we propose APHQ-ViT, a novel PTQ approach based on importance estimation with Average Perturbation Hessian (APH). Specifically, we first thoroughly analyze the current approximation approaches with Hessian loss, and propose an improved average perturbation Hessian loss. To deal with the quantization of the post-GELU activations, we design an MLP Reconstruction (MR) method by replacing the GELU function in MLP with ReLU and reconstructing it by the APH loss on a small unlabeled calibration set. Extensive experiments demonstrate that APHQ-ViT using linear quantizers outperforms existing PTQ methods by substantial margins in 3-bit and 4-bit across different vision tasks. The source code is available at https://github.com/GoatWu/APHQ-ViT.