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- High-Resolution Depth Maps Imaging via Attention-Based Hierarchical Multi-Modal Fusion79
To effectively extract and combine relevant information from LR depth and HR guidance, a multi-modal attention based fusion (MMAF) strategy for hierarchical convolutional layers, including a feature enhancement block to select valuable features and a feature recalibration block to unify the similarity metrics of modalities with different appearance characteristics are proposed.
- Self-Supervised Arbitrary-Scale Point Clouds Upsampling via Implicit Neural Representation72
This paper proposes a novel approach that achieves self-supervised and magnification-flexible point clouds upsampling simultaneously, and defines two implicit neural functions to estimate projection direction and distance respectively, which can be trained by two pretext learning tasks.
- Guided Depth Map Super-Resolution: A Survey71
A comprehensive survey of recent progress in guided depth map super-resolution (GDSR), which aims to reconstruct a high-resolution depth map from a low-resolution observation with the help of a paired high- resolution color image, is presented.
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- Deep Attentional Guided Image Filtering63
This work proposes an attentional kernel learning module to generate dual sets of filter kernels from the guidance and the target and then adaptively combine them by modeling the pixelwise dependency between the two images.
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- Targeting pro-inflammatory T cells as a novel therapeutic approach to potentially resolve atherosclerosis in humans45
T cell-targeting immunotherapy can be an effective strategy to resolve AS in humans, and this work suggests that T cell-targeting immunotherapy can be an effective strategy to resolve AS in humans.
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- Super-Resolving Face Image by Facial Parsing Information16
A novel parsing map guided face super-resolution network which extracts the face prior directly from low-resolution face image for the following utilization and develops a multi-scale refine block to maintain spatial and contextual information and take advantage of multi- scale features to refine the feature representations.
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- Pruning filters with L1-norm and standard deviation for CNN compression13
A new method to judge the importance of filters that uses the standard deviation to represent the amount of information extracted by the filter and can reduce inference cost by up to 50% for VGG16 and 35% for ResNet18/34 on CIFAR10 with little accuracy loss.
- Autonomous landing of a helicopter UAV with a ground-based multisensory fusion system12
This paper proposed a multisensory fusion to autonomous landing of an UAV, which includes an infrared camera, an Ultra-wideband radar that measure distance between UAV and Ground-Based system, an PAN-Tilt Unit (PTU) and an ultra- wideband radar distance module.
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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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