Accurate oceanic forecasting is critical for climate monitoring and disaster early-warning. However, ocean spatiotemporal forecasting encounters the double challenges of modeling complex dynamical systems and ensuring computational efficiency. We present Koopman–Fourier Time-Differentiable (KFTD) Network, a time-continuous two-stage paradigm that decouples interpolation from prediction to achieve efficient and scalable spatiotemporal modeling. We map complex nonlinear dynamics into the Koopman linear space and exploit Fourier analysis to enable continuous-time interpolation at arbitrary sub-steps. A lightweight residual network consumes the high-fidelity intermediate states to yield the final forecast. Unlike diffusion models, KFTD eliminates multi-step noise sampling and directly evolves the system in continuous time, yielding a 4× computational speed-up. We further introduce a D-PP Loss that supports arbitrary PDE constraints in an end-to-end manner, breaking the physical-consistency bottleneck of pure data-driven approaches. Empirical results on four ocean datasets confirm that our continuous-time framework reduces MSE by an average of 5.6% (up to 12.7% for SST) and improves efficiency over MCVD by 76.25%.

The explosive growth of multi-modal data in the era of big data has significantly heightened the urgent need for efficient cross-modal retrieval methods. While traditional real-valued retrieval approaches struggle with high storage costs and slow query speeds, hashing techniques have emerged as a promising solution by mapping high-dimensional data into compact binary codes. Among various hashing paradigms, short-length hashing offers superior advantages in terms of retrieval speed and storage efficiency, making it particularly suitable for resource-constrained edge devices and large-scale real-time applications. However, existing short-length hashing methods typically suffer from weak classification boundaries and significant information loss due to the extremely limited capacity of the hash bits. Most state-of-the-art methods treat the hash code as a holistic vector, failing to maximize the distinctiveness of individual bits. To tackle these challenges effectively, this paper proposes a novel method termed Bit-Level Semantic Representation Hashing (BLSRH). First, by establishing a Bit Semantic Learning Network (BSLN), we enhance the representational and discriminative capabilities of each bit in short-length hash codes independently. Additionally, a contrastive learning mechanism is introduced between bits to improve semantic consistency across modalities and reduce semantic redundancy among bits. Furthermore, to preserve the manifold structure of the original data, an adapted global similarity-preserving method is designed. Finally, a modal alignment loss based on soft-constraint is proposed to bridge the heterogeneity gap and reduce quantization errors, which replaces strict discrete constraints with flexible symbolic constraints. Comprehensive experimental results on three benchmark datasets demonstrate that BLSRH significantly outperforms state-of-the-art baselines, including recent transformer-based approaches, particularly in low-bit scenarios.

Accurate near-real-time precipitation retrieval has been enhanced by satellite-based technologies.However, infrared-based algorithms have low accuracy due to weak relations with surface precipitation, whereas passive microwave and radar-based methods are more accurate but limited in range.This challenge motivates the Precipitation Retrieval Expansion (PRE) task, which aims to enable accurate, infrared-based full-disc precipitation retrievals beyond the scanning swath.We introduce Multimodal Knowledge Expansion, a two-stage pipeline with the proposed PRE-Net model.In the Swath-Distilling stage, PRE-Net transfers knowledge from a multimodal data integration model to an infrared-based model within the scanning swath via Coordinated Masking and Wavelet Enhancement (CoMWE).In the Full-Disc Adaptation stage, Self-MaskTune refines predictions across the full disc by balancing multimodal and full-disc infrared knowledge.Experiments on the introduced PRE benchmark demonstrate that PRE-Net significantly advanced precipitation retrieval performance, outperforming leading products like PERSIANN-CCS, PDIR, and IMERG.The code will be available at https://github.com/Zjut-MultimediaPlus/PRE-Net.

Amodal instance segmentation has emerged as a critical task in the field of segmentation, facilitating the understanding of complex real-world scenes. Although numerous innovative designs and improvements have been introduced by incorporating techniques such as transformers, existing networks are still constrained to linear pattern modeling and struggle to effectively capture complex nonlinear relationships. Motivated by the strong performance of Kolmogorov–Arnold Networks (KANs), which redefine the learning paradigm by employing stacks of nonlinear, learnable activation functions derived from the Kolmogorov–Arnold representation theorem, we aim to address these limitations. Specifically, in this paper, we explore the underutilized potential of KANs to improve amodal instance segmentation architectures. We study existing amodal instance segmentation pipelines and integrate KANs to enhance amodal feature representations. Additionally, we investigate replacing traditional predictors in amodal instance segmentation with KAN-based predictors. Experiments on amodal instance segmentation datasets demonstrates the superiority of our proposed KANet.

Recently, remote sensing cross-modal retrieval has received incredible attention from researchers. However, the unique nature of remote-sensing images leads to many semantic confusion zones in the semantic space, which greatly affects retrieval performance. We propose a novel scene-aware aggregation network (SWAN) to reduce semantic confusion by improving scene perception capability. In visual representation, a visual multiscale fusion module (VMSF) is presented to fuse visual features with different scales as a visual representation backbone. Meanwhile, a scene fine-grained sensing module (SFGS) is proposed to establish the associations of salient features at different granularity. A scene-aware visual aggregation representation is formed by the visual information generated by these two modules. In textual representation, a textual coarse-grained enhancement module (TCGE) is designed to enhance the semantics of text and to align visual information. Furthermore, as the diversity and differentiation of remote sensing scenes weaken the understanding of scenes, a new metric, namely, scene recall is proposed to measure the perception of scenes by evaluating scene-level retrieval performance, which can also verify the effectiveness of our approach in reducing semantic confusion. By performance comparisons, ablation studies and visualization analysis, we validated the effectiveness and superiority of our approach on two datasets, RSICD and RSITMD. The source code is available at https://github.com/kinshingpoon/SWAN-pytorch.

Cross-modal hashing has received widespread attentions on cross-modal retrieval task due to its superior retrieval efficiency and low storage cost. However, most existing cross-modal hashing methods learn binary codes directly from multimedia data, which cannot fully utilize the semantic knowledge of the data. Furthermore, they cannot learn the ranking based similarity relevance of data points with multi-label. And they usually use a relax constraint of hash code which causes non-negligible quantization loss in the optimization. In this paper, a hashing method called Deep Adversarial Discrete Hashing (DADH) is proposed to address these issues for cross-modal retrieval. The proposed method uses adversarial training to learn features across modalities and ensure the distribution consistency of feature representations across modalities. We also introduce a weighted cosine triplet constraint which can make full use of semantic knowledge from the multi-label to ensure the precise ranking relevance of item pairs. In addition, we use a discrete hashing strategy to learn the discrete binary codes without relaxation, by which the semantic knowledge from label in the hash codes can be preserved while the quantization loss can be minimized. Ablation experiments and comparison experiments on two cross-modal databases show that the proposed DADH improves the performance and outperforms several state-of-the-art hashing methods for cross-modal retrieval.

The bone development is a continuous process, however, discrete labels are usually used to represent bone ages. This inevitably causes a semantic gap between actual situation and label representation scope. In this paper, we present a novel method named as overlap classification network to narrow the semantic gap in bone age assessment. In the proposed network, discrete bone age labels (such as 0-228 month) are considered as a sequence that is used to generate a series of subsequences. Then the proposed network makes use of the overlapping information between adjacent subsequences and output several bone age ranges at the same time for one case. The overlapping part of these age ranges is considered as the final predicted bone age. The proposed method without any preprocessing can achieve a much smaller mean absolute error compared with state-of-the-art methods on a public dataset.

No abstract available.

Advancements in technology resulted in a sharp growth in the number of digital cameras at people's disposal all across the world. Consequently, the huge storage space consumed by the videos from these devices on video repositories make the job of video processing and analysis to be time-consuming. Furthermore, this also slows down the video browsing and retrieval. Video summarization plays a very crucial role in solving these issues. Despite the number of video summarization approaches proposed up to the present time, the goal is to take a long video and generate a video summary in form of a short video skim without losing the meaning or the message transmitted by the original lengthy video. This is done by selecting the important frames called key-frames. The approach proposed by this work performs automatic summarization of digital videos based on detected objects' deep features. To this end, we apply sparse subspace clustering with an automatically estimated number of clusters to the objects' deep features. The summary generated from our scheme will store the meta-data for each short video inferred from the clustering results. In this paper, we also suggest a new video dataset for video summarization. We evaluate the performance of our work using the TVSum dataset and our video summarization dataset.