Generalized Few-shot Semantic Segmentation (GFSS) extends Few-shot Semantic Segmentation (FSS) to simultaneously segment unseen classes and seen classes during evaluation. Previous works leverage additional branch or prototypical aggregation to eliminate the constrained setting of FSS. However, representation division and embedding prejudice, which heavily results in poor performance of GFSS, have not been synthetical considered. We address the aforementioned problems by jointing the prototypical kernel learning and open-set foreground perception. Specifically, a group of learnable kernels is proposed to perform segmentation with each kernel in charge of a stuff class. Then, we explore to merge the prototypical learning to the update of base-class kernels, which is consistent with the prototype knowledge aggregation of few-shot novel classes. In addition, a foreground contextual perception module cooperating with conditional bias based inference is adopted to perform class-agnostic as well as open-set foreground detection, thus to mitigate the embedding prejudice and prevent novel targets from being misclassified as background. Moreover, we also adjust our method to the Class Incremental Few-shot Semantic Segmentation (CIFSS) which takes the knowledge of novel classes in a incremental stream. Extensive experiments on PASCAL-5iand COCO-20idatasets demonstrate that our method performs better than previous state-of-the-art.

Radar semantic segmentation is a challenging task in environmental understanding, due as the radar data is noisy and suffers measurement ambiguities, which could lead to poor feature learning. To better tackle such difficulties, we present a novel and high-performance Transformer-based Radar Semantic Segmentation method, named TransRSS, to effectively and efficiently feature extraction for radar segmentation. Our approach first introduces the transformer into radar semantic segmentation and deeply integrates the merits of the Convolutional Neural Network (CNN) and transformer to extract more discriminative and global-level semantic features. On the one hand, it takes advantage of the CNN with flexible receptive fields to process images thanks to the shift convolution scheme. On the other hand, it takes advantage of the transformer to model long-range dependency with the self-attention mechanism. Meanwhile, we propose a Dual Position Attention module to aggregate rich context interdependencies between the multi-view features, which achieves an implicit mechanism for adaptively feature aggregation. Extensive experiments on the CARRADA dataset and RADIal dataset demonstrate that our TransRSS surpasses the state-of-the-art (SOTA) radar segmentation methods with remarkable margins.