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Current encoder-decoder methods for image captioning mai-nly consist of an object detection module (two-stage), or rely on big models with large-scale datasets to improve the effectiveness, which leads to increasing computation costs and cannot ...

Few-shot classification aims to recognize the query sample through a limited amount of support data, where a prototype classifier is commonly applied. However, although the prototype classifier is simple and non-parametric, it does not fully utilize the ...

Semantic segmentation of remote sensing images (RSIs) is vital for geospatial intelligence. However, traditional methods face challenges with mixed pixels and complex land cover types. Convolutional neural networks and transformers have led the field of ...

Remote sensing semantic segmentation technology has been widely applied in various fields such as urban planning, land source management and agriculture. Due to the large number of ground objects with different scales and boundaries in remote sensing ...

Retrieval Augmented Generation (RAG) has become a common practice to alleviate the hallucination of Large Language Models (LLMs). The retrieval phase of RAG, however, usually solely depends on the original query, which, to some extent, suffers ...

In graph classification, the out-of-distribution (OOD) issue is attracting great attention. To address this issue, a prevailing idea is to learn stable features, on the assumption that they are substructures causally determining the label and that their ...

Sequential recommendation systems aim to predict users’ next likely interaction based on their history. However, these systems face data sparsity and cold-start problems. Utilizing data from other domains, known as multi-domain methods, is useful for ...

The rapid development of the Vision Transformer backbones has enabled the capture of feature information with global dependencies, leading to excellent performance in salient object detection tasks. However, it fails to adequately emphasize fine ...

• Vision Transformer lacks in its ability to focus on local information.
• The Hungarian algorithm is effectively employed for feature fusion.
• Utilizing random edge neighborhood sampling for localization of local details.
• The ...

Advancements in Earth observation technologies have greatly enhanced the potential of integrating hyperspectral (HS) images with Light Detection and Ranging (LiDAR) data for land use and land cover classification. Despite this, most existing ...

• A newly Cross-modal feature aggregation is introduced for HS and LiDAR.
• Two feature aggregation strategies refine spatial location details.
• INNs feature enhancement is to preserve local contextual integrity.

This paper presents quadrature rules for the space of functions underlying triangular Gregory patches, also called Gregory triangles. We provide numerical and where available symbolic quadrature rules not only for the space spanned by the fifteen ...

• TCP/IP-based closed-loop system enables immersive VR control of machinery.
• Remote operation framework enhances agricultural safety and productivity.
• The VR system provides 3D spatial information suitable for remote control.
• ...

In agricultural settings, the unstructured nature of certain production environments, along with the high complexity and inherent risks of production tasks, poses significant challenges to achieving full automation and effective on-site machine ...

• The method of detecting rice panicle density for unmanned harvesters is developed.
• An improved rice panicle detection model (RP-YOLO) based on YOLOv5n is proposed.
• World coordinates are converted to image coordinates and rice ...

Rice panicle density is one of the essential bases for the automatic speed regulation of unmanned harvesters, making density detection crucial for intelligent upgrades. Currently, existing methods for detecting rice panicle density do not meet ...

This paper addresses the problem of recognizing multiple objects and multiple instances from point clouds. Whereas existing methods utilize descriptors on 3D fields or pointwise voting to achieve this task, our framework takes advantage of both ...

• Addresses the problem of recognizing multiple objects and multiple instances from point clouds.
• Design a new robust descriptor called Direction-Enhanced Fast Point Feature Histogram (OE-FPFH).
• Propose a novel single point voting ...

Normal estimation has been one of the key tasks in point cloud analysis, while it is challenging when facing with severe noises or complex regions. The challenges mainly come from the selection of supporting points for estimation, that is, ...

• Proposing a hierarchically geometric-aware fitting scheme for normal estimation on point cloud.
• Proposing HG module for aggregating and updating features when shrinking graph scale.
• Introducing patch-perceptual loss to infer the ...

The noise and high randomness of the stock market are primary obstacles to profitability. These factors cause stock returns to consist of short-term predictable and stock-specific residual parts. Therefore, it is beneficial to separate and model ...

• We propose to separate stock returns into predictable and residual parts.
• We present a novel stock movement separation and prediction method.
• Our method significantly outperforms the best baselines.
• Our method are verified ...

Cross-Domain Recommendation (CDR) has been proposed to improve the recommendation accuracy in the target domain (the sparser dataset) by benefiting from the auxiliary information transferred or the knowledge learned from one or many source domains (the ...

Accurate workload forecasting is critical for efficient resource management in cloud computing systems, enabling effective scheduling and autoscaling. Despite recent advances with transformer-based forecasting models, challenges remain due to the non-...

Temporal Knowledge Graph (TKG) reasoning is a crucial task that aims to predict future facts based on historical information. In the process of reasoning over TKGs, we identify two types of facts that need to be predicted: 1) recurring facts and 2) ...