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.

Clothes-changing person re-identification (CC Re-ID) focuses on recognizing pedestrians in a long-term with changes in clothes. Prior arts extract clothes-irrelevant features either by introducing extra modality or clothing labels, having their respective limitations. Instead, we seek to extract clothes-irrelevant features without additional input. We first analyze and find that one impediment to extracting clothes-irrelevant features is the co-occurrence of samples with the same clothes and the same identity. Inspired by this observation, we propose a novel CC Re-ID approach using no additional input. We introduce theSlice-and-Align Framework (SA), which employs a straightforward and intuitive prior: the upper and lower clothes of a person are usually different. SA is a dual-stream framework that slices the original image into upper and lower halves, and then aligns them to extract clothes-irrelevant features. On image CC Re-ID datasets, SA outperforms methods without additional input by a large margin and is comparable to or even better than methods with additional input. Besides, SA also outperforms state-of-the-art on video CC Re-ID task.

Gait recognition has attracted increasing attention from academia and industry as a human recognition technology from a distance in non-intrusive ways without requiring cooperation. Although advanced methods have achieved impressive success in lab scenarios, most of them perform poorly in the wild. Recently, some Convolution Neural Networks (ConvNets) based methods have been proposed to address the issue of gait recognition in the wild. However, the temporal receptive field obtained by convolution operations is limited for long gait sequences. If directly replacing convolution blocks with visual transformer blocks, the model may not enhance a local temporal receptive field, which is important for covering a complete gait cycle. To address this issue, we design a Global-Local Temporal Receptive Field Network (GLGait). GLGait employs a Global-Local Temporal Module (GLTM) to establish a global-local temporal receptive field, which mainly consists of a Pseudo Global Temporal Self-Attention (PGTA) and a temporal convolution operation. Specifically, PGTA is used to obtain a pseudo global temporal receptive field with less memory and computation complexity compared with a multi-head self-attention (MHSA). The temporal convolution operation is used to enhance the local temporal receptive field. Besides, it can also aggregate pseudo global temporal receptive field to a true holistic temporal receptive field. Furthermore, we also propose a Center-Augmented Triplet Loss (CTL) in GLGait to reduce the intra-class distance and expand the positive samples in the training stage. Extensive experiments show that our method obtains state-of-the-art results on in-the-wild datasets, i.e., Gait3D and GREW. The code is available at https://github.com/bgdpgz/GLGait.

Gait recognition has attracted increasing attention from academia and industry as a human recognition technology from a distance in non-intrusive ways without requiring cooperation. Although advanced methods have achieved impressive success in laboratory scenarios, most of them perform poorly in the wild. Prior arts focus on modifying model structure for better extraction of global temporal and partial spatial representations in gait sequences while the aggregation of global spatial and partial temporal information is overlooked. In this paper, we propose a Synthesis Pyramid Pooling framework, named SPP. With no change to the backbone, SPP uses Global Temporal Pooling operation (TP), Horizontal Spatial Pooling operation (HSP), Global Spatial Pooling operation (SP), and Horizontal Temporal Pooling operation (HTP) to extract both global-partial and spatial-temporal gait information. Besides, we propose an Interval Sampling Strategy (ISS) to effectively extract temporal information in HTP. Extensive experiments show that our method obtains state-of-the-art results on two in-the-wild datasets, i.e. Gait3D and GREW, respectively.

In this work, we investigate the user identity linkage task across different social media platforms based on heterogeneous multi-modal posts and social connections. This task is non-trivial due to the following two challenges. 1) As each user involves both intra multi-modal posts and inter social connections, how to accurately fulfil the user representation learning from both intra and inter perspectives constitutes the main challenge. And 2) even representations distributed on different platforms of the same identity tend to be distinct (i.e., the semantic gap problem) owing to discrepant data distribution of different platforms. Hence, how to alleviate the semantic gap problem poses another tough challenge. To this end, we propose a novel adversarial-enhanced hybrid graph network (AHG-Net), consisting of three key components: user representation extraction, hybrid user representation learning, and adversarial learning. Specifically, AHG-Net first employs advanced deep learning techniques to extract the user's intermediate representations from his/her heterogeneous multi-modal posts and social connections. Then AHG-Net unifies the intra-user representation learning and inter-user representation learning with a hybrid graph network. Finally, AHG-Net adopts adversarial learning to encourage the learned user presentations of the same identity to be similar using a semantic discriminator. Towards evaluation, we create a multi-modal user identity linkage dataset by augmenting an existing dataset with 62,021 images collected from Twitter and Foursquare. Extensive experiments validate the superiority of the proposed network. Meanwhile, we release the dataset, codes, and parameters to facilitate the research community.