Xu Liu

Yanbiao Ma, Licheng Jiao, Fang Liu et al.

In scenarios with long-tailed distributions, the model's ability to identify tail classes is limited due to the under-representation of tail samples. Class rebalancing, information augmentation, and other techniques have been proposed to facilitate models to learn the potential distribution of tail classes. The disadvantage is that these methods generally pursue models with balanced class accuracy on the data manifold, while ignoring the ability of the model to resist interference. By constructing noisy data manifold, we found that the robustness of models trained on unbalanced data has a long-tail phenomenon. That is, even if the class accuracy is balanced on the data domain, it still has bias on the noisy data manifold. However, existing methods cannot effectively mitigate the above phenomenon, which makes the model vulnerable in long-tailed scenarios. In this work, we propose an Orthogonal Uncertainty Representation (OUR) of feature embedding and an end-to-end training strategy to improve the long-tail phenomenon of model robustness. As a general enhancement tool, OUR has excellent compatibility with other methods and does not require additional data generation, ensuring fast and efficient training. Comprehensive evaluations on long-tailed datasets show that our method significantly improves the long-tail phenomenon of robustness, bringing consistent performance gains to other long-tailed learning methods.

Jilong Wang, Saihui Hou, Yan Huang et al.

Gait recognition is to seek correct matches for query individuals by their unique walking patterns. However, current methods focus solely on extracting individual-specific features, overlooking ``interpersonal" relationships. In this paper, we propose a novel $\textbf{Relation Descriptor}$ that captures not only individual features but also relations between test gaits and pre-selected gait anchors. Specifically, we reinterpret classifier weights as gait anchors and compute similarity scores between test features and these anchors, which re-expresses individual gait features into a similarity relation distribution. In essence, the relation descriptor offers a holistic perspective that leverages the collective knowledge stored within the classifier's weights, emphasizing meaningful patterns and enhancing robustness. Despite its potential, relation descriptor poses dimensionality challenges since its dimension depends on the training set's identity count. To address this, we propose Farthest gait-Anchor Selection to identify the most discriminative gait anchors and an Orthogonal Regularization Loss to increase diversity within gait anchors. Compared to individual-specific features extracted from the backbone, our relation descriptor can boost the performance nearly without any extra costs. We evaluate the effectiveness of our method on the popular GREW, Gait3D, OU-MVLP, CASIA-B, and CCPG, showing that our method consistently outperforms the baselines and achieves state-of-the-art performance.

Xuqian Ren, Shaopeng Yang, Saihui Hou et al.

Previous gait recognition methods primarily trained on labeled datasets, which require painful labeling effort. However, using a pre-trained model on a new dataset without fine-tuning can lead to significant performance degradation. So to make the pre-trained gait recognition model able to be fine-tuned on unlabeled datasets, we propose a new task: Unsupervised Gait Recognition (UGR). We introduce a new cluster-based baseline to solve UGR with cluster-level contrastive learning. But we further find more challenges this task meets. First, sequences of the same person in different clothes tend to cluster separately due to the significant appearance changes. Second, sequences taken from 0° and 180° views lack walking postures and do not cluster with sequences taken from other views. To address these challenges, we propose a Selective Fusion method, which includes Selective Cluster Fusion (SCF) and Selective Sample Fusion (SSF). With SCF, we merge matched clusters of the same person wearing different clothes by updating the cluster-level memory bank with a multi-cluster update strategy. And in SSF, we merge sequences taken from front/back views gradually with curriculum learning. Extensive experiments show the effectiveness of our method in improving the rank-1 accuracy in walking with different coats condition and front/back views conditions.

Xuqian Ren, Saihui Hou, Chunshui Cao et al.

Gait recognition is instrumental in crime prevention and social security, for it can be conducted at a long distance to figure out the identity of persons. However, existing datasets and methods cannot satisfactorily deal with the most challenging cloth-changing problem in practice. Specifically, the practical gait models are usually trained on automatically labeled data, in which the sequences' views and cloth conditions of each person have some restrictions. To be concrete, the cross-view sub-dataset only has normal walking condition without cloth-changing, while the cross-cloth sub-dataset has cloth-changing sequences but only in front views. As a result, the cloth-changing accuracy cannot meet practical requirements. In this work, we formulate the problem as Realistic Cloth-Changing Gait Recognition (abbreviated as RCC-GR) and we construct two benchmarks: CASIA-BN-RCC and OUMVLP-RCC, to simulate the above setting. Furthermore, we propose a new framework called Progressive Feature Learning that can be applied with off-the-shelf backbones to improve their performance in RCC-GR. Specifically, in our framework, we design Progressive Mapping and Progressive Uncertainty to extract cross-view features and then extract cross-cloth features on the basis. In this way, the feature from the cross-view sub-dataset can first dominate the feature space and relieve the uneven distribution caused by the adverse effect from the cross-cloth sub-dataset. The experiments on our benchmarks show that our framework can effectively improve recognition performance, especially in the cloth-changing conditions.

Xu Liu, Licheng Jiao, Fang Liu

Polarimetric SAR data has the characteristics of all-weather, all-time and so on, which is widely used in many fields. However, the data of annotation is relatively small, which is not conducive to our research. In this paper, we have collected five open polarimetric SAR images, which are images of the San Francisco area. These five images come from different satellites at different times, which has great scientific research value. We annotate the collected images at the pixel level for image classification and segmentation. For the convenience of researchers, the annotated data is open source https://github.com/liuxuvip/PolSF.

Xu Liu, Tong Zhou, Yuanxin Wang et al.

The advent of foundation models, which are pre-trained on vast datasets, has ushered in a new era of computer vision, characterized by their robustness and remarkable zero-shot generalization capabilities. Mirroring the transformative impact of foundation models like large language models (LLMs) in natural language processing, visual foundation models (VFMs) have become a catalyst for groundbreaking developments in computer vision. This review paper delineates the pivotal trajectories of VFMs, emphasizing their scalability and proficiency in generative tasks such as text-to-image synthesis, as well as their adeptness in discriminative tasks including image segmentation. While generative and discriminative models have historically charted distinct paths, we undertake a comprehensive examination of the recent strides made by VFMs in both domains, elucidating their origins, seminal breakthroughs, and pivotal methodologies. Additionally, we collate and discuss the extensive resources that facilitate the development of VFMs and address the challenges that pave the way for future research endeavors. A crucial direction for forthcoming innovation is the amalgamation of generative and discriminative paradigms. The nascent application of generative models within discriminative contexts signifies the early stages of this confluence. This survey aspires to be a contemporary compendium for scholars and practitioners alike, charting the course of VFMs and illuminating their multifaceted landscape.