The problem of extreme multi-label text classification (XMTC) is to recall some most relevant labels for a text from an extremely large label set.
To tackle this problem, we propose an effective similarity-based method to select data from the source domains.
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.
This paper describes our system submitted to task 4 of SemEval 2020: Commonsense Validation and Explanation (ComVE) which consists of three sub-tasks.
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
Despite remarkable advances in image synthesis research, existing works often fail in manipulating images under the context of large geometric transformations.
Review text has been widely studied in traditional tasks such as sentiment analysis and aspect extraction.
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.
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)
We present a system called ACE for Automatic Colloquialism and Errors detection for written Chinese.