85 papers
LiM-YOLO is a streamlined ship detection model for optical remote sensing imagery that achieves state-of-the-art accuracy with fewer parameters by shifting the detection pyramid from P3-P5 to P2-P4 to better resolve small vessels and employing Group Normalization to stabilize training on high-resolution inputs.
This paper introduces CoPeDiT, a unified 3D MRI synthesis framework that leverages a self-perceptive latent diffusion model with completeness-aware prompts to generate high-fidelity, structurally consistent images without relying on external manual guidance for missing data.
This paper proposes an entropy-and-channel-aware adaptive-rate semantic communication framework for MIMO Rayleigh fading channels that dynamically selects feature maps and symbols based on channel conditions and content complexity, while leveraging a fine-tuned multimodal large language model (MLLM) at the receiver to compensate for discarded information and optimize task performance across varying signal-to-noise ratios.
This paper proposes a model-based image compression technique for tetherless underwater remotely operated vehicles that leverages novel view synthesis priors and gradient descent optimization to achieve superior compression ratios and image quality, particularly in scenarios involving new objects within the scene.
This paper introduces PanoAffordanceNet, a novel framework and the first high-quality dataset (360-AGD) designed to enable holistic affordance grounding in 360-degree indoor environments by addressing challenges like geometric distortion and semantic dispersion through distortion-aware calibration and multi-level constraints.
The paper introduces -Occ, a robust 3D semantic occupancy prediction framework that leverages a Multi-view Masked Reconstruction module and a Feature Memory Module to maintain geometric and semantic coherence under incomplete multi-camera inputs, significantly outperforming existing methods in scenarios with missing views.
CycleULM is a novel, label-free deep learning framework that leverages CycleGAN to bridge the simulation-to-reality gap in ultrasound localisation microscopy, significantly enhancing microbubble localisation accuracy, image resolution, and processing speed for real-time clinical application without requiring paired ground truth data.
This study demonstrates that the longitudinal progression of radiologic pleuroparenchymal fibroelastosis (PPFE), quantified via automated analysis of low-dose CT scans, independently predicts increased mortality and adverse respiratory outcomes in large lung cancer screening cohorts.
The paper presents MetaSpectra+, a compact broadband metasurface camera capable of snapshot hyperspectral imaging combined with either HDR or polarization data, achieving superior reconstruction accuracy and a shorter total track length than prior narrowband multifunctional imagers.
The paper proposes DFPF-Net, a novel remote sensing change detection framework that integrates a pyramid vision transformer with a progressive fusion module and a dynamic change focus mechanism to effectively mitigate both global pseudo-changes and local noise, thereby outperforming mainstream methods across multiple datasets.
The paper introduces M2Diff, a multi-modality multi-task diffusion model that separately processes MRI and low-dose PET scans to extract and hierarchically fuse modality-specific features, thereby significantly improving the fidelity of standard-dose PET reconstruction for both healthy and Alzheimer's disease populations.
This paper proposes a two-stage probabilistic framework that first reconstructs incomplete satellite fire observations using various deep learning models and then predicts wildfire spread, effectively bridging the domain gap caused by partial observability to restore prediction accuracy to near-clean-input levels.
KV-Lock is a training-free framework for DiT-based video diffusion models that dynamically adjusts background key-value locking and classifier-free guidance scales based on hallucination detection to simultaneously enhance foreground quality and maintain background consistency.
This paper presents an enhanced deep learning framework, POLISH, which utilizes patch-wise training and nonlinear intensity transformations to achieve robust, high-dynamic-range, wide-field radio interferometric imaging, demonstrating its ability to significantly increase the discovery rate of strong gravitational lenses compared to traditional methods.
This paper introduces a quantum-native image matching framework for neutral-atom analog computers that converts images into sparse point clouds encoded in Rydberg atom arrays, utilizing time-evolved correlation matrices and static structure factors as constant-length fingerprints to achieve efficient, scale-invariant retrieval and machine learning with minimal atom counts.
CryoNet.Refine is a novel one-step diffusion model that automates and accelerates the refinement of atomic structures against cryo-EM density maps, outperforming traditional tools like Phenix in both model-map correlation and geometric quality while supporting diverse protein and nucleic acid complexes.
UltraUPConvNet is a computationally efficient, multi-task framework based on UPerNet and ConvNeXt that simultaneously performs ultrasound tissue segmentation and disease prediction, achieving state-of-the-art performance on a large-scale dataset with reduced computational overhead.
This paper presents TransUNet-GradCAM, a hybrid Vision Transformer-U-Net model that effectively segments diabetic foot ulcers by combining global attention with local feature extraction, achieving high accuracy on internal and external datasets while providing explainable visualizations for clinical utility.
This study introduces a variance-penalized GAN based on pyramid pix2pix that generates high-fidelity HER2-specific immunohistochemistry (IHC) images from routine hematoxylin and eosin (H&E) slides, effectively mitigating mode collapse and outperforming baseline models to enable cost-effective, scalable precision oncology diagnostics.
The paper introduces DeepSparse, a foundation model for sparse-view CBCT reconstruction that utilizes a novel DiCE network and a HyViP pretraining framework to achieve superior image quality with reduced radiation exposure while overcoming the computational and generalization limitations of existing methods.