AinnoSeg: Panoramic Segmentation with High Perfomance
This work addresses detail-related issues in panoramic segmentation for computer vision applications, but it appears incremental as it builds on existing methods with modifications.
The paper tackles the problem of insufficient detail processing in panoramic segmentation, such as occluded object segmentation and boundary pixel classification, by introducing AinnoSeg, which achieves state-of-the-art performance on the ADE20K dataset.
Panoramic segmentation is a scene where image segmentation tasks is more difficult. With the development of CNN networks, panoramic segmentation tasks have been sufficiently developed.However, the current panoramic segmentation algorithms are more concerned with context semantics, but the details of image are not processed enough. Moreover, they cannot solve the problems which contains the accuracy of occluded object segmentation,little object segmentation,boundary pixel in object segmentation etc. Aiming to address these issues, this paper presents some useful tricks. (a) By changing the basic segmentation model, the model can take into account the large objects and the boundary pixel classification of image details. (b) Modify the loss function so that it can take into account the boundary pixels of multiple objects in the image. (c) Use a semi-supervised approach to regain control of the training process. (d) Using multi-scale training and reasoning. All these operations named AinnoSeg, AinnoSeg can achieve state-of-art performance on the well-known dataset ADE20K.