In the context of Omni-Directional Image (ODI) Super-Resolution (SR), the unique challenge arises from the non-uniform oversampling characteristics caused by EquiRectangular Projection (ERP). Considerable efforts in designing complex spherical convolutions or polyhedron reprojection offer significant performance improvements but at the expense of cumbersome processing procedures and slower inference speeds. Under these circumstances, this paper proposes a new ODI-SR model characterized by its capacity to perform Fast and Arbitrary-scale ODI-SR processes, denoted as FAOR. The key innovation lies in adapting the implicit image function from the planar image domain to the ERP image domain by incorporating spherical geometric priors at both the latent representation and image reconstruction stages, in a low-overhead manner. Specifically, at the latent representation stage, we adopt a pair of pixel-wise and semantic-wise sphere-to-planar distortion maps to perform affine transformations on the latent representation, thereby incorporating it with spherical properties. Moreover, during the image reconstruction stage, we introduce a geodesic-based resampling strategy, aligning the implicit image function with spherical geometrics without introducing additional parameters. As a result, the proposed FAOR outperforms the state-of-the-art ODI-SR models with a much faster inference speed. Extensive experimental results and ablation studies have demonstrated the effectiveness of our design.

Multi-view clustering is valued for uncovering latent common semantics lying in multi-view data, which has been a hot topic in unsupervised learning. However, when dealing with incremental streaming views, existing approaches typically require reconstructing the view data and aggregating streaming representations, leading to misalignment between representation and clusters. More importantly, conducting the clustering process frequently results in significant time consumption. To address these issues, we propose a novel method called Binary Continual Stream-View Clustering (BCSVC). Specifically, we design a continual clustering method that seamlessly unifies streaming representation learning and cluster assignment within a single framework. We also introduce a variance-weighted center updating mechanism to smooth the frequent clustering operation and absorb the semantics of previous views. In addition, to reduce the time and space expenditure on computation and storage, binary code for clustering representations is introduced, which can also significantly improve the computational efficiency of continuous updates in streaming scenarios. Last but not least, comprehensive theoretical analysis and extensive experimental results demonstrate its superior performance under various scenarios.

Full-reference image quality assessment (FR-IQA) models generally operate by measuring the visual differences between a degraded image and its reference. However, existing FR-IQA models including both the classical ones (e.g., PSNR and SSIM) and deep-learning based measures (e.g., LPIPS and DISTS) still exhibit limitations in capturing the full perception characteristics of the human visual system (HVS). In this paper, instead of designing a new FR-IQA measure, we aim to explore a generalized human visual attention estimation strategy to mimic the process of human quality rating and enhance existing IQA models. In particular, we model human attention generation by measuring the statistical dependency between the degraded image and the reference image. The dependency is captured in a training-free manner by our proposed sliced maximal information coefficient and exhibits surprising generalization in different IQA measures. Experimental results verify the performance of existing IQA models can be consistently improved when our attention module is incorporated. The source code is available at https://github.com/KANGX99/SMIC.

Traditionally, disk failure prediction accuracy is used to evaluate disk failure prediction model. However, accuracy may not reflect their practical usage (protecting against failures, rather than only predicting failures) in cloud storage systems. In this paper, we propose two new metrics for disk failure prediction models: migration rate, which measures how much at-risk data is protected as a result of correct failure predictions, and mismigration rate, which measures how much data is migrated needlessly as a result of false failure predictions. To demonstrate their effectiveness, we compare disk failure prediction methods: (a) a classification tree (CT) model vs. a state-of-the-art recurrent neural network (RNN) model, and (b) a proposed residual life prediction model based on gradient boosted regression trees (GBRTs) vs. RNN. While prediction accuracy experiments favor the RNN model, migration rate experiments can favor the CT and GBRT models (depending on transfer rates). We conclude that prediction accuracy can be a misleading metric. Moreover, the proposed GBRT model offers a practical improvement in disk failure prediction in real-world data centers.