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Found 11 papers, 3 papers with code
GUDN: A novel guide network with label reinforcement strategy for extreme multi-label text classification
Large-scale pre-trained models have brought a new trend to this problem.
Prior Omission of Dissimilar Source Domain(s) for Cost-Effective Few-Shot Learning
To tackle this problem, we propose an effective similarity-based method to select data from the source domains.
RRScell method for automated single-cell profiling of multiplexed immunofluorescence cancer tissue
Multiplexed immuno-fluorescence tissue imaging, allowing simultaneous detection of molecular properties of cells, is an essential tool for characterizing the complex cellular mechanisms in translational research and clinical practice.
CUHK at SemEval-2020 Task 4: CommonSense Explanation, Reasoning and Prediction with Multi-task Learning
This paper describes our system submitted to task 4 of SemEval 2020: Commonsense Validation and Explanation (ComVE) which consists of three sub-tasks.
Towards Multi-pose Guided Virtual Try-on Network
Given an input person image, a desired clothes image, and a desired pose, the proposed Multi-pose Guided Virtual Try-on Network (MG-VTON) can generate a new person image after fitting the desired clothes into the input image and manipulating human poses.
Ranked #1 on
Virtual Try-on
on Deep-Fashion
Soft-Gated Warping-GAN for Pose-Guided Person Image Synthesis
Despite remarkable advances in image synthesis research, existing works often fail in manipulating images under the context of large geometric transformations.
Aspect and Sentiment Aware Abstractive Review Summarization
Review text has been widely studied in traditional tasks such as sentiment analysis and aspect extraction.
GPSP: Graph Partition and Space Projection based Approach for Heterogeneous Network Embedding
Finally, we concatenate the projective vectors from bipartite subnetworks with the ones learned from homogeneous subnetworks to form the final representation of the heterogeneous network.
NLPTEA 2017 Shared Task -- Chinese Spelling Check
This paper provides an overview along with our findings of the Chinese Spelling Check shared task at NLPTEA 2017.
Generative Adversarial Network for Abstractive Text Summarization
In this paper, we propose an adversarial process for abstractive text summarization, in which we simultaneously train a generative model G and a discriminative model D. In particular, we build the generator G as an agent of reinforcement learning, which takes the raw text as input and predicts the abstractive summarization.
Ranked #5 on
Text Summarization
on CNN / Daily Mail (Anonymized)
Abstractive Text Summarization
Generative Adversarial Network
+2
ACE: Automatic Colloquialism, Typographical and Orthographic Errors Detection for Chinese Language
We present a system called ACE for Automatic Colloquialism and Errors detection for written Chinese.
