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Found 24 papers, 17 papers with code
Reference-free Summarization Evaluation via Semantic Correlation and Compression Ratio
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.
Length Control in Abstractive Summarization by Pretraining Information Selection
In this paper, we propose a length-aware attention mechanism (LAAM) to adapt the encoding of the source based on the desired length.
CSCNET: Class-Specified Cascaded Network for Compositional Zero-Shot Learning
Inspired by this, we propose a novel A-O disentangled framework for CZSL, namely Class-specified Cascaded Network (CSCNet).
Kun: Answer Polishment for Chinese Self-Alignment with Instruction Back-Translation
In this paper, we introduce Kun, a novel approach for creating high-quality instruction-tuning datasets for large language models (LLMs) without relying on manual annotations.
Zero-shot Faithfulness Evaluation for Text Summarization with Foundation Language Model
Despite tremendous improvements in natural language generation, summarization models still suffer from the unfaithfulness issue.
Context Compression for Auto-regressive Transformers with Sentinel Tokens
The quadratic complexity of the attention module makes it gradually become the bulk of compute in Transformer-based LLMs during generation.
Reducing Sensitivity on Speaker Names for Text Generation from Dialogues
Changing speaker names consistently throughout a dialogue should not affect its meaning and corresponding outputs for text generation from dialogues.
Global and Local Mixture Consistency Cumulative Learning for Long-tailed Visual Recognitions
In this paper, our goal is to design a simple learning paradigm for long-tail visual recognition, which not only improves the robustness of the feature extractor but also alleviates the bias of the classifier towards head classes while reducing the training skills and overhead.
Ranked #1 on
Long-tail Learning
on CIFAR-10-LT (ρ=10)
In-sample Curriculum Learning by Sequence Completion for Natural Language Generation
Curriculum learning has shown promising improvements in multiple domains by training machine learning models from easy samples to hard ones.
Adjacent Slice Feature Guided 2.5D Network for Pulmonary Nodule Segmentation
In this paper, we design an adjacent slice feature fusion model to introduce information from adjacent slices.
Taxonomy of Abstractive Dialogue Summarization: Scenarios, Approaches and Future Directions
Abstractive dialogue summarization is to generate a concise and fluent summary covering the salient information in a dialogue among two or more interlocutors.
Hierarchical Similarity Learning for Aliasing Suppression Image Super-Resolution
As a highly ill-posed issue, single image super-resolution (SISR) has been widely investigated in recent years.
Textural-Perceptual Joint Learning for No-Reference Super-Resolution Image Quality Assessment
Image super-resolution (SR) has been widely investigated in recent years.
Post-Training Dialogue Summarization using Pseudo-Paraphrasing
Previous dialogue summarization techniques adapt large language models pretrained on the narrative text by injecting dialogue-specific features into the models.
Learning Weighting Map for Bit-Depth Expansion within a Rational Range
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.
Cross-SRN: Structure-Preserving Super-Resolution Network with Cross Convolution
It is challenging to restore low-resolution (LR) images to super-resolution (SR) images with correct and clear details.
Segment, Magnify and Reiterate: Detecting Camouflaged Objects the Hard Way
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.
Leveraging Line-Point Consistence To Preserve Structures for Wide Parallax Image Stitching
Generating high-quality stitched images with natural structures is a challenging task in computer vision.
DDRel: A New Dataset for Interpersonal Relation Classification in Dyadic Dialogues
In this paper, we propose the task of relation classification of interlocutors based on their dialogues.
Ranked #1 on
Dialog Relation Extraction
on DDRel
Multi-turn Response Selection using Dialogue Dependency Relations
In this paper, we propose a dialogue extraction algorithm to transform a dialogue history into threads based on their dependency relations.
Matching Questions and Answers in Dialogues from Online Forums
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.
Sequential Dual Deep Learning with Shape and Texture Features for Sketch Recognition
We develop dual deep networks with memorable gated recurrent units (GRUs), and sequentially feed these two types of features into the dual networks, respectively.
A Fast Ellipse Detector Using Projective Invariant Pruning
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.
