Lu Li

Yongshuo Zhu, Lu Li, Keyan Chen et al.

Remote sensing image change captioning (RSICC) aims to articulate the changes in objects of interest within bi-temporal remote sensing images using natural language. Given the limitations of current RSICC methods in expressing general features across multi-temporal and spatial scenarios, and their deficiency in providing granular, robust, and precise change descriptions, we introduce a novel change captioning (CC) method based on the foundational knowledge and semantic guidance, which we term Semantic-CC. Semantic-CC alleviates the dependency of high-generalization algorithms on extensive annotations by harnessing the latent knowledge of foundation models, and it generates more comprehensive and accurate change descriptions guided by pixel-level semantics from change detection (CD). Specifically, we propose a bi-temporal SAM-based encoder for dual-image feature extraction; a multi-task semantic aggregation neck for facilitating information interaction between heterogeneous tasks; a straightforward multi-scale change detection decoder to provide pixel-level semantic guidance; and a change caption decoder based on the large language model (LLM) to generate change description sentences. Moreover, to ensure the stability of the joint training of CD and CC, we propose a three-stage training strategy that supervises different tasks at various stages. We validate the proposed method on the LEVIR-CC and LEVIR-CD datasets. The experimental results corroborate the complementarity of CD and CC, demonstrating that Semantic-CC can generate more accurate change descriptions and achieve optimal performance across both tasks.

Guangshuai Gao, Qingjie Liu, Zhenghui Hu et al.

Object counting, which aims to count the accurate number of object instances in images, has been attracting more and more attention. However, challenges such as large scale variation, complex background interference, and non-uniform density distribution greatly limit the counting accuracy, particularly striking in remote sensing imagery. To mitigate the above issues, this paper proposes a novel framework for dense object counting in remote sensing images, which incorporates a pyramidal scale module (PSM) and a global context module (GCM), dubbed PSGCNet, where PSM is used to adaptively capture multi-scale information and GCM is to guide the model to select suitable scales generated from PSM. Moreover, a reliable supervision manner improved from Bayesian and Counting loss (BCL) is utilized to learn the density probability and then compute the count expectation at each annotation. It can relieve non-uniform density distribution to a certain extent. Extensive experiments on four remote sensing counting datasets demonstrate the effectiveness of the proposed method and the superiority of it compared with state-of-the-arts. Additionally, experiments extended on four commonly used crowd counting datasets further validate the generalization ability of the model. Code is available at https://github.com/gaoguangshuai/PSGCNet.

Amirabbas Hojjati, Lu Li, Ibrahim Hameed et al.

EEG signals capture brain activity with high temporal and low spatial resolution, supporting applications such as neurological diagnosis, cognitive monitoring, and brain-computer interfaces. However, effective analysis is hindered by limited labeled data, high dimensionality, and the absence of scalable models that fully capture spatiotemporal dependencies. Existing self-supervised learning (SSL) methods often focus on either spatial or temporal features, leading to suboptimal representations. To this end, we propose EEG-VJEPA, a novel adaptation of the Video Joint Embedding Predictive Architecture (V-JEPA) for EEG classification. By treating EEG as video-like sequences, EEG-VJEPA learns semantically meaningful spatiotemporal representations using joint embeddings and adaptive masking. To our knowledge, this is the first work that exploits V-JEPA for EEG classification and explores the visual concepts learned by the model. Evaluations on the publicly available Temple University Hospital (TUH) Abnormal EEG dataset show that EEG-VJEPA outperforms existing state-of-the-art models in classification accuracy. Beyond classification accuracy, EEG-VJEPA captures physiologically relevant spatial and temporal signal patterns, offering interpretable embeddings that may support human-AI collaboration in diagnostic workflows. These findings position EEG-VJEPA as a promising framework for scalable, trustworthy EEG analysis in real-world clinical settings.

Yongxiang Gu, Xiaolin Qin, Yuncong Peng et al.

As one of the prevalent components, Feature Pyramid Network (FPN) is widely used in current object detection models for improving multi-scale object detection performance. However, its feature fusion mode is still in a misaligned and local manner, thus limiting the representation power. To address the inherit defects of FPN, a novel architecture termed Content-Augmented Feature Pyramid Network (CA-FPN) is proposed in this paper. Firstly, a Global Content Extraction Module (GCEM) is proposed to extract multi-scale context information. Secondly, lightweight linear spatial Transformer connections are added in the top-down pathway to augment each feature map with multi-scale features, where a linearized approximate self-attention function is designed for reducing model complexity. By means of the self-attention mechanism in Transformer, there is no longer need to align feature maps during feature fusion, thus solving the misaligned defect. By setting the query scope to the entire feature map, the local defect can also be solved. Extensive experiments on COCO and PASCAL VOC datasets demonstrated that our CA-FPN outperforms other FPN-based detectors without bells and whistles and is robust in different settings.