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Found 16 papers, 7 papers with code
Fourier-Net: Fast Image Registration with Band-limited Deformation
Specifically, instead of our Fourier-Net learning to output a full-resolution displacement field in the spatial domain, we learn its low-dimensional representation in a band-limited Fourier domain.
U-Net vs Transformer: Is U-Net Outdated in Medical Image Registration?
On the public 3D IXI brain dataset for atlas-based registration, we show that the performance of the vanilla U-Net is already comparable with that of state-of-the-art transformer-based networks (such as TransMorph), and that the proposed LKU-Net outperforms TransMorph by using only 1. 12% of its parameters and 10. 8% of its mult-adds operations.
Personalized Showcases: Generating Multi-Modal Explanations for Recommendations
In this paper, to further enrich explanations, we propose a new task named personalized showcases, in which we provide both textual and visual information to explain our recommendations.
Structure Unbiased Adversarial Model for Medical Image Segmentation
It consists of a spatial transformation block followed by an intensity distribution rendering module.
Adaptive Affinity Loss and Erroneous Pseudo-Label Refinement for Weakly Supervised Semantic Segmentation
Semantic segmentation has been continuously investigated in the last ten years, and majority of the established technologies are based on supervised models.
Semantic Attention and Scale Complementary Network for Instance Segmentation in Remote Sensing Images
To address the above problems, we propose an end-to-end multi-category instance segmentation model, namely Semantic Attention and Scale Complementary Network, which mainly consists of a Semantic Attention (SEA) module and a Scale Complementary Mask Branch (SCMB).
Equality before the Law: Legal Judgment Consistency Analysis for Fairness
We propose to simulate judges from different groups with legal judgment prediction (LJP) models and measure the judicial inconsistency with the disagreement of the judgment results given by LJP models trained on different groups.
How Does NLP Benefit Legal System: A Summary of Legal Artificial Intelligence
Legal Artificial Intelligence (LegalAI) focuses on applying the technology of artificial intelligence, especially natural language processing, to benefit tasks in the legal domain.
A Two-Stream Meticulous Processing Network for Retinal Vessel Segmentation
In this paper, we propose a novel two-stream Meticulous-Processing Network (MP-Net) for tackling this problem.
JEC-QA: A Legal-Domain Question Answering Dataset
We present JEC-QA, the largest question answering dataset in the legal domain, collected from the National Judicial Examination of China.
Identify the cells' nuclei based on the deep learning neural network
The cells' nuclei identification task is also kind of image segmentation.
CAIL2019-SCM: A Dataset of Similar Case Matching in Legal Domain
In this paper, we introduce CAIL2019-SCM, Chinese AI and Law 2019 Similar Case Matching dataset.
SkrGAN: Sketching-rendering Unconditional Generative Adversarial Networks for Medical Image Synthesis
Generative Adversarial Networks (GANs) have the capability of synthesizing images, which have been successfully applied to medical image synthesis tasks.
CE-Net: Context Encoder Network for 2D Medical Image Segmentation
In this paper, we propose a context encoder network (referred to as CE-Net) to capture more high-level information and preserve spatial information for 2D medical image segmentation.
Ranked #1 on
Optic Disc Segmentation
on Messidor
A Hierarchical Framework for Relation Extraction with Reinforcement Learning
The whole extraction process is decomposed into a hierarchy of two-level RL policies for relation detection and entity extraction respectively, so that it is more feasible and natural to deal with overlapping relations.
Ranked #2 on
Relation Extraction
on NYT24
Entity Extraction using GAN
Hierarchical Reinforcement Learning
+3
Emotional Chatting Machine: Emotional Conversation Generation with Internal and External Memory
Perception and expression of emotion are key factors to the success of dialogue systems or conversational agents.
