Kun Bai

Fangfei Lin, Bing Bai, Kun Bai et al.

Hierarchical clustering recursively partitions data at an increasingly finer granularity. In real-world applications, multi-view data have become increasingly important. This raises a less investigated problem, i.e., multi-view hierarchical clustering, to better understand the hierarchical structure of multi-view data. To this end, we propose a novel neural network-based model, namely Contrastive Multi-view Hyperbolic Hierarchical Clustering (CMHHC). It consists of three components, i.e., multi-view alignment learning, aligned feature similarity learning, and continuous hyperbolic hierarchical clustering. First, we align sample-level representations across multiple views in a contrastive way to capture the view-invariance information. Next, we utilize both the manifold and Euclidean similarities to improve the metric property. Then, we embed the representations into a hyperbolic space and optimize the hyperbolic embeddings via a continuous relaxation of hierarchical clustering loss. Finally, a binary clustering tree is decoded from optimized hyperbolic embeddings. Experimental results on five real-world datasets demonstrate the effectiveness of the proposed method and its components.

Wenguang Tao, Xiaotian Wang, Tian Yan et al.

As a key research direction in the field of multi-object tracking (MOT), UAV-based multi-object tracking has significant application value in the analysis and understanding of urban intelligent transportation systems. However, in complex UAV perspectives, challenges such as small target scale variations, occlusions, nonlinear crossing motions, and motion blur severely hinder the stability of multi-object tracking. To address these challenges, this paper proposes a novel multi-object tracking framework, SocialTrack, aimed at enhancing the tracking accuracy and robustness of small targets in complex urban traffic environments. The specialized small-target detector enhances the detection performance by employing a multi-scale feature enhancement mechanism. The Velocity Adaptive Cubature Kalman Filter (VACKF) improves the accuracy of trajectory prediction by incorporating a velocity dynamic modeling mechanism. The Group Motion Compensation Strategy (GMCS) models social group motion priors to provide stable state update references for low-quality tracks, significantly improving the target association accuracy in complex dynamic environments. Furthermore, the Spatio-Temporal Memory Prediction (STMP) leverages historical trajectory information to predict the future state of low-quality tracks, effectively mitigating identity switching issues. Extensive experiments on the UAVDT and MOT17 datasets demonstrate that SocialTrack outperforms existing state-of-the-art (SOTA) methods across several key metrics. Significant improvements in MOTA and IDF1, among other core performance indicators, highlight its superior robustness and adaptability. Additionally, SocialTrack is highly modular and compatible, allowing for seamless integration with existing trackers to further enhance performance.

Jing Xu, Tszhang Guo, Yong Xu et al.

Deep Convolutional Neural Networks (DCNNs) and their variants have been widely used in large scale face recognition(FR) recently. Existing methods have achieved good performance on many FR benchmarks. However, most of them suffer from two major problems. First, these methods converge quite slowly since they optimize the loss functions in a high-dimensional and sparse Gaussian Sphere. Second, the high dimensionality of features, despite the powerful descriptive ability, brings difficulty to the optimization, which may lead to a sub-optimal local optimum. To address these problems, we propose a simple yet efficient training mechanism called MultiFace, where we approximate the original high-dimensional features by the ensemble of low-dimensional features. The proposed mechanism is also generic and can be easily applied to many advanced FR models. Moreover, it brings the benefits of good interpretability to FR models via the clustering effect. In detail, the ensemble of these low-dimensional features can capture complementary yet discriminative information, which can increase the intra-class compactness and inter-class separability. Experimental results show that the proposed mechanism can accelerate 2-3 times with the softmax loss and 1.2-1.5 times with Arcface or Cosface, while achieving state-of-the-art performances in several benchmark datasets. Especially, the significant improvements on large-scale datasets(e.g., IJB and MageFace) demonstrate the flexibility of our new training mechanism.

Bing Bai, Guanhua Zhang, Ye Lin et al.

Nowadays, news apps have taken over the popularity of paper-based media, providing a great opportunity for personalization. Recurrent Neural Network (RNN)-based sequential recommendation is a popular approach that utilizes users' recent browsing history to predict future items. This approach is limited that it does not consider the societal influences of news consumption, i.e., users may follow popular topics that are constantly changing, while certain hot topics might be spreading only among specific groups of people. Such societal impact is difficult to predict given only users' own reading histories. On the other hand, the traditional User-based Collaborative Filtering (UserCF) makes recommendations based on the interests of the "neighbors", which provides the possibility to supplement the weaknesses of RNN-based methods. However, conventional UserCF only uses a single similarity metric to model the relationships between users, which is too coarse-grained and thus limits the performance. In this paper, we propose a framework of deep neural networks to integrate the RNN-based sequential recommendations and the key ideas from UserCF, to develop Collaborative Sequential Recommendation Networks (CSRNs). Firstly, we build a directed co-reading network of users, to capture the fine-grained topic-specific similarities between users in a vector space. Then, the CSRN model encodes users with RNNs, and learns to attend to neighbors and summarize what news they are reading at the moment. Finally, news articles are recommended according to both the user's own state and the summarized state of the neighbors. Experiments on two public datasets show that the proposed model outperforms the state-of-the-art approaches significantly.

Jian Liang, Yuren Cao, Chenbin Zhang et al.

Authentication is a task aiming to confirm the truth between data instances and personal identities. Typical authentication applications include face recognition, person re-identification, authentication based on mobile devices and so on. The recently-emerging data-driven authentication process may encounter undesired biases, i.e., the models are often trained in one domain (e.g., for people wearing spring outfits) while required to apply in other domains (e.g., they change the clothes to summer outfits). To address this issue, we propose a novel two-stage method that disentangles the class/identity from domain-differences, and we consider multiple types of domain-difference. In the first stage, we learn disentangled representations by a one-versus-rest disentangle learning (OVRDL) mechanism. In the second stage, we improve the disentanglement by an additive adversarial learning (AAL) mechanism. Moreover, we discuss the necessity to avoid a learning dilemma due to disentangling causally related types of domain-difference. Comprehensive evaluation results demonstrate the effectiveness and superiority of the proposed method.