Incremental Object Detection (IOD) aims to continuously learn new object classes without forgetting previously learned ones. A persistent challenge is catastrophic forgetting, primarily attributed to background shift in conventional detectors. While pseudo-labeling mitigates this in dense detectors, we identify a novel, distinct source of forgetting specific to DETR-like architectures: background foregrounding. This arises from the exhaustiveness constraint of the Hungarian matcher, which forcibly assigns every ground truth target to one prediction, even when predictions primarily cover background regions (i.e., low IoU). This erroneous supervision compels the model to misclassify background features as specific foreground classes, disrupting learned representations and accelerating forgetting. To address this, we propose a Quality-guided Min-Cost Max-Flow (Q-MCMF) matcher. To avoid forced assignments, Q-MCMF builds a flow graph and prunes implausible matches based on geometric quality. It then optimizes for the final matching that minimizes cost and maximizes valid assignments. This strategy eliminates harmful supervision from background foregrounding while maximizing foreground learning signals. Extensive experiments on the COCO dataset under various incremental settings demonstrate that our method consistently outperforms existing state-of-the-art approaches.

Online Class-Incremental Learning (OCIL) enables models to learn continuously from non-i.i.d. data streams. Since samples of the data streams can be seen only once, it is more suitable for real-world scenarios compared to offline learning. However, this constraint intensifies the challenge for OCIL in maintaining an appropriate balance between stability and plasticity. Moreover, under stricter memory buffer constraints in real world, current replay-based methods are less effective. While ensemble methods improve plasticity, they often struggle with stability. Inspired by the Global Workspace Theory (GWT), we propose a novel approach that enhances ensemble learning through a Global Workspace Model (GWM)-a shared, implicit memory that guides the learning of multiple student models. The GWM is formed by fusing the parameters of all students within each training batch, capturing the historical learning trajectory and serving as a dynamic anchor for knowledge consolidation. Like the broadcasting mechanism of GWT, the GWM is redistributed periodically to students, stabilizing learning and promoting cross-task consistency. In addition, we introduce a multi-level collaborative distillation mechanism. It enforces peer-to-peer consistency among students and preserves historical knowledge by aligning each student with the GWM. As a result, student models remain adaptable to new tasks while maintaining previously learned knowledge, striking a better balance between stability and plasticity. Extensive experiments on three standard OCIL benchmarks show that our method delivers significant performance improvement for several OCIL models across various memory budgets. The code is available at https://github.com/susususushi/GWM.

Semantic segmentation is a fundamental task in computer vision and finds extensive applications in scene understanding, medical image analysis, and remote sensing. With the advent of deep learning, significant advancements have been made in segmentation tasks. However, deep learning models require a substantial amount of labeled data for training, and accurately annotating datasets is labor-intensive and costly. Recently, numerous studies have explored the semantic segmentation task through the lens of semi-supervised learning, with the pseudo-labeling (PL) method emerging as a straightforward and widely applicable approach. This paper provides a comprehensive review and analysis of various PL methods and their applications in semi-supervised semantic segmentation (SSSS) from multiple angles. Initially, it captures the essence of individual model self-training and the collaborative training of multiple models from a model-centric viewpoint. Next, it explores strategies for refining or dismissing unreliable methods. Then, it categorizes techniques for addressing noisy PL data and inspects improvements in PL methods from the perspective of data augmentation. It further provides insights into optimization strategies. Furthermore, it examines PL methods from an application-oriented standpoint, such as in medical image segmentation and remote sensing image segmentation. Lastly, this paper evaluates the performance of cutting-edge methods on public datasets and concludes by discussing the challenges and potential directions for future research.

Change detection (CD) is an essential field in remote sensing, with a primary focus on identifying areas of change in bitemporal image pairs captured at varying intervals of the same region. The data annotation process for CD tasks is both time-consuming and labor-intensive. To better utilize the scarce labeled data and abundant unlabeled data, we introduce an adaptive semi-supervised learning (SSL) method, AdaSemiCD, to improve pseudo-label usage and optimize the training process. Initially, due to the extreme class imbalance inherent in CD, the model is more inclined to focus on the background class, and it is easy to confuse the boundary of the target object. Considering these two points, we develop a measurable evaluation metric for pseudo-labels that enhances the representation of information entropy by class rebalancing and amplification of ambiguous areas, assigning greater weights to prospective change objects. Subsequently, to enhance the reliability of sample wise pseudo-labels, we introduce the AdaFusion module, to dynamically identify the most uncertain region and substitute it with more trustworthy content. Lastly, to ensure better training stability, we introduce the AdaEMA module, which updates the teacher model using only batches of trusted samples. Experimental results on ten public CD datasets validate the efficacy and generalizability of our proposed adaptive training framework.

Semantic segmentation of river ice image serves as a critical technological foundation for hydrological monitoring and ice flood early warning system. Current publicly available river ice datasets predominantly utilize UAV-captured image and ground-based photographic observations. To address the limitations of spatial coverage in existing datasets, we present NWPU_YRCC_GFICE - a satellite remote sensing dataset constructed from multi-spectral GF-2 satellite images. The dataset innovatively categorizes river ice into six fine-grained classes across freeze-thaw cycles and covers river ice data from Yellow River (Ningxia-Inner Mongolia section) spanning the past 10 years. We further establish a comprehensive deep learning benchmark, which evaluates 33 state-of-the-art segmentation models and two improved segmentation models based on YOLO and Segformer architecture, separately. Experiments are conducted on the NWPU_YRCC_GFICE dataset and three public river ice datasets (NWPU_YRCC_EX, NWPU_YRCC2, and Alberta river ice segmentation dataset). The proposed models exhibit excellent performance, surpassing the state-of-the-art methods. The presented NWPU_YRCC_GFICE dataset and benchmark enriches the river ice dataset and favors in promoting fine-grained river ice segmentation research from satellite view. Our dataset and code is available at https://github.com/ASGOLabMultisourceCooperationGroup/NWPU_YRCC_GFICE.

Camouflaged object detection (COD) aims to segment camouflaged objects which exhibit very similar patterns with the surrounding environment. Recent research works have shown that enhancing the feature representation via the frequency information can greatly alleviate the ambiguity problem between the foreground objects and the background. With the emergence of vision foundation models, like InternImage, Segment Anything Model etc, adapting the pretrained model on COD tasks with a lightweight adapter module shows a novel and promising research direction. Existing adapter modules mainly care about the feature adaptation in the spatial domain. In this paper, we propose a novel frequency-guided spatial adaptation method for COD task. Specifically, we transform the input features of the adapter into frequency domain. By grouping and interacting with frequency components located within non overlapping circles in the spectrogram, different frequency components are dynamically enhanced or weakened, making the intensity of image details and contour features adaptively adjusted. At the same time, the features that are conducive to distinguishing object and background are highlighted, indirectly implying the position and shape of camouflaged object. We conduct extensive experiments on four widely adopted benchmark datasets and the proposed method outperforms 26 state-of-the-art methods with large margins. Code will be released.

The semantic segmentation of remote sensing (RS) images is a challenging and hot issue due to the large amount of unlabeled data and domain variation. Unsupervised domain adaptation (UDA) has proven to be advantageous in leveraging unlabeled information from the target domain. However, traditional approaches of independently fine-tuning UDA models in the source and target domains have a limited effect on the result. In this article, we propose a hybrid training strategy that boosts self-training methods with domain fusion images. First, we introduce a novel dual-domain image fusion (DDF) strategy to effectively utilize the original image, the style-transferred image, and the intermediate-domain information. Second, to further refine the precision of pseudolabels, we present a region-specific reweighting strategy that assigns different weights to pseudolabel regions based on their spatial context. Finally, we conduct a series of extensive benchmark experiments and ablation studies on the ISPRS Vaihingen and Potsdam datasets. These results show the efficiency of our approach and establish a practical basis for implementing semantic segmentation in remote sensors.

Real-time semantic segmentation of remote-sensing images demands a trade-off between speed and accuracy, which makes it challenging. Apart from manually designed networks, researchers seek to adopt neural architecture search (NAS) to discover a real-time semantic segmentation model with optimal performance automatically. Most existing NAS methods stack up no more than two types of searched cells, omitting the characteristics of resolution variation. This paper proposes the Hierarchical shared Architecture Search (HAS) method to automatically build a real-time semantic segmentation model for remote sensing images. Our model contains a lightweight backbone and a multi-scale feature fusion module. The lightweight backbone is carefully designed with low computational cost. The multi-scale feature fusion module is searched using the NAS method, where only the blocks from the same layer share identical cells. Extensive experiments reveal that our searched real-time semantic segmentation model of remote sensing images achieves the state-of-the-art trade-off between accuracy and speed. Specifically, on the LoveDA, Potsdam, and Vaihingen datasets, the searched network achieves 54.5% mIoU, 87.8% mIoU, and 84.1% mIoU, respectively, with an inference speed of 132.7 FPS. Besides, our searched network achieves 72.6% mIoU at 164.0 FPS on the CityScapes dataset and 72.3% mIoU at 186.4 FPS on the CamVid dataset.

Detecting changes in heterogeneous images without the supervision of changed label is a challenging yet critical task for quick responding natural disaster relief. Nevertheless, most of available unsupervised heterogeneous change detection methods strong rely on the quality of pseudo labels, and they suffer from performance degradation, even irreversible model collapse, when encounter the low-quality pseudo labels, leading to unreliable detection results. In order to improve the reliability of unsupervised heterogeneous change detection, in this paper, we propose a novel change detection paradigm based on sample synthesis and knowledge transfer. We address the issue of label reliability by artificially creating a changed region and assigning labels rather than constructing pseudo labels. These constructed labels guide the network in automatically learning the correspondence between heterogeneous images, confirming the reliability of changed regions. Moreover, an augmentation with synthetic samples on real samples makes it possible to generate more transferable samples while reducing the domain gap coarsely. A dual-branch joint training with feature contrastive learning is further developed to transfer the knowledge of changes from the synthetic sample domain to real sample domain. Experimental results on five public datasets demonstrate that our proposed method has superior performance when compared with available state-of-the-art methods. Our code is available at https://github.com/zhangqiiii/SS-KT.

Semantic change detection (SCD) is a challenging task in remote sensing image (RSI) interpretation, which adopts multitemporal images to detect, locate, and analyze pixel-level land-cover “from-to” changes. In SCD, the severe class imbalance problem and the occurrence of confusing categories are very typical, making it challenging to accurately distinguish the easily confused categories with limited semantic context information. However, previous works did not address these issues in depth. This article proposes a novel SCD method named semi-supervised contrastive learning (SSCLNet), in which a simple and effective SCD network is designed as a strong baseline, and a semi-supervised contrastive learning module of semantic segmentation (SS) is presented to enhance the distinguishability of categories. Our baseline extracts semantic context through high-resolution network (HRNet), gets change information simply through an absolute difference, and then directly performs SCD based on the fusion of semantic context and change information. To utilize the semantic context information of the unlabeled non-changed regions, we employ a self-training (ST) method for semi-supervised SS. To learn distinguishable feature representations for easily confused categories, we present contrastive learning with an adaptive sampling strategy for SS. It selects challenging negative samples for each category from the other categories that exhibit similar features or attributes. The sampling space includes both the labeled changed samples and the non-changed samples predicted by ST. The comprehensive experiments on the SECOND and the Landsat-SCD dataset demonstrate that the proposed SSCLNet achieves the state-of-the-art (SOTA) performance, with a significant improvement of 2.07% and 4.15% in the score value, respectively.

Change detection based on heterogeneous images is of great importance in some applications, such as disaster monitoring and damage assessment. However, due to the huge modality discrepancy in heterogeneous images, it is difficult to accurately detect the changed regions. In this paper, we analyze the interference of modality-alignment and changed areas to each other, and propose a progressive modality-alignment based unsupervised change detection model for heterogeneous images. Specifically, the modality alignment is achieved in an iterative manner, which can improve the detection accuracy progressively. To reduce the influence of modality discrepancy and the changed regions to each other, a pseudo-label self-learning strategy is designed, where the pseudo-labels learned by the model itself are used to act as a guidance of change detection, and they are in turn refined by the proposed progressive model. Experimental results on different real heterogeneous images verify the effectiveness and robustness of proposed method.

Semantic segmentation has long been a hot topic, most methods are the region based method, which lost connection information to their neighbors. In this paper we propose to encode context information into convolutional networks on this semantic labeling task. Firstly, we propose the nonlocal convolution kernel, which extracts feature from larger neighbor regions without introducing more parameters. Then we build up a context aware module, which takes both local patch and nonlocal neighbor information into account. At last we embed the module into convolutional networks and tested the improvement on benchmark datasets.