Siyue Yu

Ensuring railway safety requires reliable monitoring of trains in critical safety areas, such as station throat zones and railway crossings. Compared with cameras, roadside LiDAR can more reliably capture the geometry of trains under low-light, high-speed, and adverse weather conditions. However, industrial LiDAR solutions still primarily use the background comparison technique, which compares each sample against a pre-recorded clean map and then applies a size-based filter. Such approaches are highly sensitive to point cloud background changes arising from varying LiDAR installation distances, train speeds, and surface materials, often resulting in fragmented clustering and missed detections. In this paper, train detection is reformulated as a point-level semantic segmentation problem. A lightweight 3D segmentation network that directly predicts train points from raw data is designed, and clustering-based post-processing is applied to generate train-level events in real time. Experiments on real railway data under various operating conditions show that the proposed method achieves higher detection accuracy and greater robustness than traditional compare-based methods and representative deep learning benchmark methods, and is therefore suitable for practical railway safety monitoring.

Continual Semantic Segmentation (CSS) suffers from catastrophic forgetting, particularly challenging for traditional per-pixel methods. Our prior work, CoMasTRe (CVPR 2024), introduced a query-based approach leveraging objectness by disentangling CSS into objectness learning and class recognition stages. While effective, CoMasTRe exhibited performance limitations due to feature forgetting within its pixel decoder. This paper presents CoMasTRe+, an enhanced framework specifically designed to overcome this limitation. The core contribution is a novel plugin, the Mixture of Continual Adapters (MoCA), integrated into the pixel decoder. MoCA is a dynamic architecture that mitigates feature forgetting by learning task-specific expert adapters. Crucially, MoCA employs a task-aware routing strategy and a novel adaptive routing distillation objective, tailored for continual learning, to preserve specialized feature representations across sequential tasks. CoMasTRe+ further enhances the class decoder using MoCA for improved recognition and simplicity. We extensively evaluate CoMasTRe+ on PASCAL VOC and ADE20K for continual semantic and panoptic segmentation. Experiments demonstrate that CoMasTRe+ effectively addresses the identified feature forgetting issue, significantly outperforms the original CoMasTRe, and achieves state-of-the-art results compared to both per-pixel and query-based baselines.

Weakly supervised semantic segmentation has witnessed great achievements with image-level labels. Several recent approaches use the CLIP model to generate pseudo labels for training an individual segmentation model, while there is no attempt to apply the CLIP model as the backbone to directly segment objects with image-level labels. In this paper, we propose WeCLIP and its advanced version WeCLIP+, to build the single-stage pipeline for weakly supervised semantic segmentation. For WeCLIP, the frozen CLIP model is applied as the backbone for semantic feature extraction, and a new light decoder is designed to interpret extracted semantic features for final prediction. Meanwhile, we utilize the above frozen backbone to generate pseudo labels for training the decoder. Such labels are fixed during training. We then propose a refinement module (RFM) to optimize them dynamically. For WeCLIP+, we introduce the frozen DINO model to achieve more comprehensive semantic feature extraction. The frozen DINO is combined with the frozen CLIP as the backbone, followed by a shared decoder to make predictions with less training cost. Moreover, a strengthened refinement module (RFM+) is designed to revise online pseudo labels with extra guidance from DINO features. Extensive experiments show that both WeCLIP and WeCLIP+ significantly outperform other approaches with less training cost. Particularly, WeCLIP+ gets mIoU of 83.9% on VOC 2012 test set and 56.3% on COCO val set. Additionally, these two approaches also obtain promising results for fully supervised settings. The code is available at https://github.com/zbf1991/WeCLIP.

Weakly supervised semantic segmentation has attracted a lot of attention recently. Previous methods can be divided into two types, which are single-stage training and multi-stage training. In this paper, we focus on multi-stage training for image-level weakly supervised semantic segmentation. Many recent methods have tried to use transformer architecture as the backbone for CAM generation since it can capture global relationships to refine CAM accurately. However, we observe that such a backbone still fails to generate complete and smooth CAM. We argue that this is because the attention mechanism in the transformer can only pay attention to the most discriminative relationships. It is difficult to capture semantic-level long-range pair-wise relationships under image-level supervision. Thus, we propose an adversarial erasing transformer network called AETN, where an erasing attention mechanism is designed to establish more extensive pair-wise relationships. To cope with erasing, more target features will be forced to activate. Thus, better feature representation can be obtained for more accurate CAM generation. Besides, to further help our network learn better feature representation, we propose a self-consistent learning mechanism based on different augmentations. In this way, our AETN outperforms recent methods. Our AETN achieves 73.0 mIoU on the PASCAL VOC 2012 val set and 73.9 mIoU on the PASCAL VOC 2012 test set. Code is available a https://github.com/siyueyu/AETN.