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- Recent Advances for Quantum Neural Networks in Generative Learning143
This paper interprets these QGLMs, covering quantum circuit Born machines, quantum generative adversarial networks, quantum Boltzmann machines, and quantum variational autoencoders, as the quantum extension of classical generative learning models, and explores their intrinsic relations and their fundamental differences.
- GANet: Goal Area Network for Motion Forecasting117
A new goal area-based framework, named Goal Area Network (GANet), for motion forecasting, which models goal areas as preconditions for trajectory prediction, performing more robustly and accurately.
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- XLRS-Bench: Could Your Multimodal LLMs Understand Extremely Large Ultra-High-Resolution Remote Sensing Imagery?65
XLRS-Bench is presented: a comprehensive benchmark for evaluating the perception and reasoning capabilities of MLLMs in ultra-high-resolution RS scenarios, with a primary emphasis on advanced cognitive processes that facilitate real-world decision-making and the capture of spatiotemporal changes.
- Conditional Variational Capsule Network for Open Set Recognition63
This work uses the variational autoencoder framework, with a set of gaussian priors as the approximation for the posterior distribution, to control the compactness of the features of the same class around the center of the gaussians, thus controlling the ability of the classifier in detecting samples from unknown classes.
- User experience model and design strategies for virtual reality-based cultural heritage exhibition52
This study synthesizes three-level coding and quantitative analysis findings from grounded theory, builds a detailed model of the VRCHE UX using theoretical coding, and proposes design strategies.
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- Effect of pulse repetition frequency of high-intensity focused ultrasound on in vitro thrombolysis50
The PRF of delivered HIFU bursts at the same output energy has a significant effect on the thrombi motion, bubble cavitation activities, and subsequently thrombolysis efficiencies.
- A greedy-based stable multi-path routing protocol in mobile ad hoc networks50
A Greedy-based Backup Routing (GBR) protocol is presented that considers both route length and link lifetime to achieve high route stability and has excellent performance in terms of route lifetime, packet delivery ratio, and control overhead.
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- Accuracy Assessment of GlobeLand30 2010 Land Cover over China Based on Geographically and Categorically Stratified Validation Sample Data43
The methods for and results from the first country-wide and statistically based accuracy assessment of GlobeLand30 2010 land cover dataset over China indicate that Globe land cover circa 2010 in China quite accurately, although estimates of accuracy indicators were lower as expected.
- A Lightweight 1-D Convolution Augmented Transformer with Metric Learning for Hyperspectral Image Classification42
This work believes it is the first to combine the metric learning and the transformer model in hyperspectral image classification, and to improve the model classification performance when the available training samples are limited, it uses the 1-D convolution and Mish activation function.
- Debiasing Multimodal Large Language Models via Penalization of Language Priors39
This investigation sheds light on the instability of MLLMs across various decoding configurations, and achieves significant performance improvements-surpassing previously reported results-and raises concerns about the fairness of current evaluation practices.
- Cauchy sparse NMF with manifold regularization: A robust method for hyperspectral unmixing39
This work proposes a robust hyperspectral unmixing model called Cauchy Sparse NMF with manifold regularization (MCSNMF), which incorporates the prior geometric structure into robust subspace learning and outperforms the state-of-the-art NMF based un Mixing methods on both simulated datasets and real scene images.
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- An automatic cervical cell classification model based on improved DenseNet12134
Compared with other state-of-the-art algorithms, the A2SDNet121 model performs better in the multi-classification task of cervical cells, which can significantly improve the accuracy and efficiency of cervical cancer screening.
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- BT Expansion: a Sound and Complete Algorithm for Behavior Planning of Intelligent Robots with Behavior Trees31
This paper proposes BT expansion, an automated planning approach to building intelligent robot behaviors with BTs, and proves the soundness and completeness through the state-space formulation of BTs.
- Mavors: Multi-granularity Video Representation for Multimodal Large Language Model30
Experiments across diverse benchmarks demonstrate Mavors' superiority in maintaining both spatial fidelity and temporal continuity, significantly outperforming existing methods in tasks requiring fine-grained spatio-temporal reasoning.
- Numerical Simulation and Analysis of Fish-Like Robots Swarm30
The results show that at close spacing, the fish in the back can obtain a large wake from the front fish, but suffers large lateral power loss from the lateral fish, and when the spacing is large, both the wake and pressure caused by the front and side fishes become small.
- Learning Capsules for SAR Target Recognition29
A convolutional neural network extension based on Hinton's capsule network is developed to capture spatial relationships specialized in classification between different entities in a SAR image to achieve the accurate and robust classification of SAR images without significantly increasing network complexity.
- MagicFusion: Boosting Text-to-Image Generation Performance by Fusing Diffusion Models28
This work proposes a simple yet effective method called Saliency-aware Noise Blending (SNB), which can empower the fused text-guided diffusion models to achieve more controllable generation by blending the predicted noises of two diffusion models in a saliency-aware manner.
- An image thresholding approach based on Gaussian mixture model27
An image thresholding approach based on Gaussian mixture model (GMM) to solve the problem of noisy and uneven grayscale images by adopting expectation maximization algorithm with a simple initialization strategy and utilizing Bayesian criteria to generate the binary map.
- Tackling Noisy Labels With Network Parameter Additive Decomposition26
To decouple the memorization of clean data and mislabeled data, and further reduce the side effect of mislabeled data, additive decomposition on network parameters is performed.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-14. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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