Streaming View Classification with Noisy Label

In many image processing tasks, e.g., 3D reconstruction of dynamic scenes, different types of descriptions, a.k.a., views, of an object are emerging in a streaming way. Streaming view learning provides an effective solution to this dynamic view problem. In this paradigm, existing streaming view learning methods typically assume that all labels are accurate. However, in many real-world applications, the initial views may be not good enough for characterizing, leading to noisy labels that degrade classification performance. How to learn a model for simultaneous view evolving and label ambiguity is critical yet unexplored. In this paper, we propose a novel method called Streaming View Classification with Noisy Label (SVCNL). We calibrate noisy labels according to the emerging of new views, thereby reflecting the dynamic changes in the data more accurately. Leveraging the sequential and non-revisitable nature of views, the method tunes existing models to inherit information from previous stages by utilizing current-stage data. It reconstructs noisy labels through a label transition matrix and establishes relationships between true labels and samples using a graph embedding strategy, progressively correcting noisy labels. Together with the theoretical analyses about generalization bounds, extensive experiments demonstrate the effectiveness of the proposed approach.