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We are living in a world progressively driven by data. Besides mining big data which cannot be altogether stored in the main memory as required by traditional offline methods, the problem of learning rare data that can only be collected over time is also very prevalent. Consequently, there is a need of online methods which can handle arriving data and offer the same accuracy as offline methods. In this paper, we introduce a new lossless online Bayesianbased classifier which uses the arriving data in a 1-by-1 manner and discards each data right after use. The lossless property of our proposed method guarantees that it can reach the same prediction model as its offline counterpart regardless of the incremental training order. Experimental results demonstrate its superior performance over many well-known state-of-theart methods in the literature.

In recent years, instance segmentation has become a key research area in computer vision. This technology has been applied in varied applications such as robotics, healthcare and intelligent driving. Instance segmentation technology not only detects the location of the object but also marks edges for each single instance, which can solve both object detection and semantic segmentation concurrently. Our survey will give a detail introduction to the instance segmentation technology based on deep learning, reinforcement learning and transformers. Further, we will discuss about its development in this field along with the most common datasets used. We will also focus on different challenges and future development scope for instance segmentation. This technology will provide a strong reference for future researchers in our survey paper.

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