Recent graph-based multi-view clustering (GMVC) methods typically encode view features into high-dimensional spaces and construct graphs based on distance similarity. However, the high dimensionality of the embeddings often leads to the hubness problem, where a few points repeatedly appear in the nearest neighbor lists of other points. We show that this negatively impacts the extracted graph structures and message passing, thus degrading clustering performance. To the best of our knowledge, we are the first to highlight the detrimental effect of hubness in GMVC methods and introduce the hubREP (hub-aware Representation Embedding and Pairing) framework. Specifically, we propose a simple yet effective encoder that reduces hubness while preserving neighborhood topology within each view. Additionally, we propose a hub-aware pairing module to maintain structure consistency across views, efficiently enhancing the view-specific representations. The proposed hubREP is lightweight compared to the conventional autoencoders used in state-of-the-art GMVC methods and can be integrated into existing GMVC methods that mostly focus on novel fusion mechanisms, further boosting their performance. Comprehensive experiments performed on eight benchmarks confirm the superiority of our method. The code is available at https://github.com/zmxu196/hubREP.

Color constancy, the human visual system's ability to perceive consistent colors under varying illumination conditions, is crucial for accurate color perception. Recently, deep learning algorithms have been introduced into this task and have achieved remarkable achievements. However, existing methods are limited by the scale of current multi-illumination datasets and model size, hindering their ability to learn discriminative features effectively and their practical value for deployment in cameras. To overcome these limitations, this paper proposes a multi-illumination color constancy approach based on self-supervised learning and knowledge distillation. This approach includes three phases: self-supervised pre-training, supervised fine-tuning, and knowledge distillation. During the pre-training phase, we train Transformer-based and U-Net based encoders by two pretext tasks: light normalization task to learn lighting color contextual representation and grayscale colorization task to acquire objects' inherent color information. For the downstream color constancy task, we fine-tune the encoders and design a lightweight decoder to obtain better illumination distributions with fewer parameters. During the knowledge distillation phase, we introduce a hybrid knowledge distillation technique to align CNN features with those of Transformer and U-Net respectively. Our proposed method outperforms state-of-the-art techniques on multi-illumination and single-illumination benchmarks. Extensive ablation studies and visualizations confirm the effectiveness of our model.

Few-shot 3D point cloud semantic segmentation is a challenging task due to the lack of labeled point clouds (support set). To segment unlabeled query point clouds, existing prototype-based methods learn 3D prototypes from point features of the support set and then measure their distances to the query points. However, such homogeneous 3D prototypes are often of low quality because they overlook the valuable heterogeneous information buried in the support set, such as semantic labels and projected 2D depth maps. To address this issue, in this paper, we propose a novel Relation Consistency-guided Heterogeneous Prototype learning framework (RCHP), which improves prototype quality by integrating heterogeneous information using large multi-modal models (e.g.CLIP). RCHP achieves this through two core components: Heterogeneous Prototype Generation module which collaborates with 3D networks and CLIP to generate heterogeneous prototypes, and Heterogeneous Prototype Fusion module which effectively fuses heterogeneous prototypes to obtain high-quality prototypes. Furthermore, to bridge the gap between heterogeneous prototypes, we introduce a Heterogeneous Relation Consistency loss, which transfers more reliable inter-class relations (i.e., inter-prototype relations) from refined prototypes to heterogeneous ones. Extensive experiments conducted on five point cloud segmentation datasets, including four indoor datasets (S3DIS, ScanNet, SceneNN, NYU Depth V2) and one outdoor dataset (Semantic3D), demonstrate the superiority and generalization capability of our method, outperforming state-of-the-art approaches across all datasets. The code will be released as soon as the paper is accepted.

Color constancy is an important ability of the human visual system to perceive constant colors across different illumination. In this paper, we study a more practical yet challenging task, removing color cast by multiple spatial-varying illumination. Previous methods are limited by the scale of the current multi-illumination datasets, which hinders them from learning more discriminative features. Instead, we first propose a self-supervised multi-illumination color constancy model that leverages multiple pretext tasks to fully explore lighting color contextual information and inherent color information without using any manual annotations. During the pre-training phase, we train multiple Transformer-based encoders by learning multiple pretext tasks: (i) the local color distortion recovery task, which is carefully designed to learn lighting color contextual representation, and (ii) the colorization task, which is utilized to acquire inherent knowledge. In the downstream color constancy task, we fine-tune the encoders and design a lightweight decoder to obtain better illumination distributions with fewer parameters. Our lightweight architecture outperforms the state-of-the-art methods on the multi-illuminant benchmark (LSMI) and got robust performance on the single illuminant benchmark (NUS-8). Additionally, extensive ablation studies and visualization results demonstrate the effectiveness of integrating lighting color contextual and inherent color information learning in a self-supervised manner.

Vehicle re-identification (Re-ID) represents the task aiming to identify the same vehicle from images captured by different cameras. Recent years have seen various feature learning-based approaches merely focusing on feature representations including global features or local features to obtain more subtle details to identify highly similar vehicles. However, few such methods consider the spatial geometrical structure relationship among local regions or between the global and local regions. By contrast, in this study, we propose a Spatial Structural Relation Network (SSR-Net) that explores the above-mentioned two kinds of relations simultaneously to learn more discriminative features by modeling the spatial structure information and global context information. In this article, we propose to adopt a Graph Convolution Network (GCN), for modeling spatial structural relationships among characteristic features. The GCN model aggregating the local and global features is shown to be more discriminative and robust to several car image transformations. To improve the performance of our proposed network, we jointly combine the classification loss with metric learning loss. Extensive experiments conducted on the public VehicleID and VeRi-776 datasets validate the effectiveness of our approach in comparison with recent works.

A Virtual Private Cloud (VPC) is a secure collection of computing, storage and network resources spanning multiple sites over Wide Area Network (WAN). With VPC, computation and services are no longer restricted to a fixed site but can be relocated dynamically across geographical sites to improve manageability, performance and fault tolerance. We propose WAVNet, a layer 2 virtual private network (VPN) which supports virtual machine live migration over WAN to realize mobility of execution environment across multiple security domains. WAVNet adopts a UDP hole punching technique to achieve direct network connection between two Internet hosts without special router configuration. We evaluate our design in an emulated WAN with 64 hosts and also in a real WAN environment with 10 machines located at seven different sites across the Asia-Pacific region. The experimental results show that WAVNet not only achieves close-to-native host-to-host network bandwidth and latency, but also guarantees more effective VM live migration than existing solutions.