In this paper, we propose a new automatic reference-free evaluation metric that compares semantic distribution between source document and summary by pretrained language models and considers summary compression ratio.
In this paper, we propose a length-aware attention mechanism (LAAM) to adapt the encoding of the source based on the desired length.
Curriculum learning has shown promising improvements in multiple domains by training machine learning models from easy samples to hard ones.
Abstractive dialogue summarization is to generate a concise and fluent summary covering the salient information in a dialogue among two or more interlocutors.
As a highly ill-posed issue, single image super-resolution (SISR) has been widely investigated in recent years.
Image super-resolution (SR) has been widely investigated in recent years.
Previous dialogue summarization techniques adapt large language models pretrained on the narrative text by injecting dialogue-specific features into the models.
Existing BDE methods have no unified solution for various BDE situations, and directly learn a mapping for each pixel from LBD image to the desired value in HBD image, which may change the given high-order bits and lead to a huge deviation from the ground truth.
It is challenging to restore low-resolution (LR) images to super-resolution (SR) images with correct and clear details.
To tackle camouflaged object detection (COD), we are inspired by humans attention coupled with the coarse-to-fine detection strategy, and thereby propose an iterative refinement framework, coined SegMaR, which integrates Segment, Magnify and Reiterate in a multi-stage detection fashion.
Generating high-quality stitched images with natural structures is a challenging task in computer vision.
In this paper, we propose the task of relation classification of interlocutors based on their dialogues.
Ranked #1 on Dialog Relation Extraction on DDRel
In this paper, we propose a dialogue extraction algorithm to transform a dialogue history into threads based on their dependency relations.
Matching question-answer relations between two turns in conversations is not only the first step in analyzing dialogue structures, but also valuable for training dialogue systems.
We develop dual deep networks with memorable gated recurrent units (GRUs), and sequentially feed these two types of features into the dual networks, respectively.
Detecting elliptical objects from an image is a central task in robot navigation and industrial diagnosis where the detection time is always a critical issue.