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Found 17 papers, 9 papers with code
基于多源知识融合的领域情感词典表示学习研究(Domain Sentiment Lexicon Representation Learning Based on Multi-source Knowledge Fusion)
“本文旨在解决领域情感词典构建任务中标注数据资源相对匮乏以及情感语义表示不充分问题, 通过多源数据领域差异计算联合权重, 融合先验情感知识和Fasttext词向量表示学习, 将情感语义知识映射到新的词向量空间, 从无标注数据中自动构建适应大数据多领域和多语言环境的领域情感词典。在中英文多领域公开数据集上的对比实验表明, 与情感词典方法和预训练词向量方法相比, 本文提出的多源知识融合的领域情感词典表示学习方法在实验数据集上的分类正确率均有明显提升, 并在多种算法、多语言、多领域和多数据集上具有较好的鲁棒性。本文还通过消融实验验证了所提出模型的各个模块在提升情感分类效果中的作用。”
SageAttention2 Technical Report: Accurate 4 Bit Attention for Plug-and-play Inference Acceleration
Second, we propose a method to smooth $Q$ and $V$, enhancing the accuracy of attention with INT4 $QK$ and FP8 $PV$.
SAM-Swin: SAM-Driven Dual-Swin Transformers with Adaptive Lesion Enhancement for Laryngo-Pharyngeal Tumor Detection
Laryngo-pharyngeal cancer (LPC) is a highly lethal malignancy in the head and neck region.
Multi-Scale Diffusion: Enhancing Spatial Layout in High-Resolution Panoramic Image Generation
Diffusion models have recently gained recognition for generating diverse and high-quality content, especially in the domain of image synthesis.
SageAttention: Accurate 8-Bit Attention for Plug-and-play Inference Acceleration
Although quantization has proven to be an effective method for accelerating model inference, existing quantization methods primarily focus on optimizing the linear layer.
SAM-FNet: SAM-Guided Fusion Network for Laryngo-Pharyngeal Tumor Detection
However, they are still limited in their capabilities to accurately locate the lesion region and capture the discriminative feature information between the global and local branches.
Unsupervised Tumor-Aware Distillation for Multi-Modal Brain Image Translation
Existing methods suffer from the problem of brain tumor deformation during translation, as they fail to focus on the tumor areas when translating the whole images.
BEND: Bagging Deep Learning Training Based on Efficient Neural Network Diffusion
The originality of BEND comes from the first use of a neural network diffusion model to efficiently build base classifiers for bagging.
Learning Multi-Modal Brain Tumor Segmentation from Privileged Semi-Paired MRI Images with Curriculum Disentanglement Learning
Specifically, in the first step, we propose to conduct reconstruction and segmentation with augmented intra-modality style-consistent images.
SFusion: Self-attention based N-to-One Multimodal Fusion Block
To solve this problem, we propose a self-attention based fusion block called SFusion.
Unsupervised Multi-Modal Medical Image Registration via Discriminator-Free Image-to-Image Translation
In this paper, we propose a novel translation-based unsupervised deformable image registration approach to convert the multi-modal registration problem to a mono-modal one.
Slice Imputation: Intermediate Slice Interpolation for Anisotropic 3D Medical Image Segmentation
We introduce a novel frame-interpolation-based method for slice imputation to improve segmentation accuracy for anisotropic 3D medical images, in which the number of slices and their corresponding segmentation labels can be increased between two consecutive slices in anisotropic 3D medical volumes.
A Compact Neural Network-based Algorithm for Robust Image Watermarking
Digital image watermarking seeks to protect the digital media information from unauthorized access, where the message is embedded into the digital image and extracted from it, even some noises or distortions are applied under various data processing including lossy image compression and interactive content editing.
TarGAN: Target-Aware Generative Adversarial Networks for Multi-modality Medical Image Translation
In this paper, we propose a novel target-aware generative adversarial network called TarGAN, which is a generic multi-modality medical image translation model capable of (1) learning multi-modality medical image translation without relying on paired data, (2) enhancing quality of target area generation with the help of target area labels.
Tips and Tricks for Webly-Supervised Fine-Grained Recognition: Learning from the WebFG 2020 Challenge
WebFG 2020 is an international challenge hosted by Nanjing University of Science and Technology, University of Edinburgh, Nanjing University, The University of Adelaide, Waseda University, etc.
Inter-slice image augmentation based on frame interpolation for boosting medical image segmentation accuracy
We introduce the idea of inter-slice image augmentation whereby the numbers of the medical images and the corresponding segmentation labels are increased between two consecutive images in order to boost medical image segmentation accuracy.
Unified Attentional Generative Adversarial Network for Brain Tumor Segmentation From Multimodal Unpaired Images
Currently, most deep models for multimodal segmentation rely on paired registered images.
