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Found 17 papers, 9 papers with code
Large-Scale Relation Learning for Question Answering over Knowledge Bases with Pre-trained Language Models
The key challenge of question answering over knowledge bases (KBQA) is the inconsistency between the natural language questions and the reasoning paths in the knowledge base (KB).
Table Fact Verification with Structure-Aware Transformer
Verifying fact on semi-structured evidence like tables requires the ability to encode structural information and perform symbolic reasoning.
Beyond the Sequence: Statistics-Driven Pre-training for Stabilizing Sequential Recommendation Model
The sequential recommendation task aims to predict the item that user is interested in according to his/her historical action sequence.
FedPIT: Towards Privacy-preserving and Few-shot Federated Instruction Tuning
Extensive experiments on real-world medical data demonstrate the effectiveness of FedPIT in improving federated few-shot performance while preserving privacy and robustness against data heterogeneity.
RBSR: Efficient and Flexible Recurrent Network for Burst Super-Resolution
The main challenge of BurstSR is to effectively combine the complementary information from input frames, while existing methods still struggle with it.
Joint Video Multi-Frame Interpolation and Deblurring under Unknown Exposure Time
Moreover, on the seemingly implausible x16 interpolation task, our method outperforms existing methods by more than 1. 5 dB in terms of PSNR.
PATS: Sensitivity-aware Noisy Learning for Pretrained Language Models
A wide range of NLP tasks benefit from the fine-tuning of pretrained language models (PLMs).
Let Me Check the Examples: Enhancing Demonstration Learning via Explicit Imitation
Inspired by the human learning process, in this paper, we introduce Imitation DEMOnstration Learning (Imitation-Demo) to strengthen demonstration learning via explicitly imitating human review behaviour, which includes: (1) contrastive learning mechanism to concentrate on the similar demonstrations.
Robust Deep Ensemble Method for Real-world Image Denoising
In particular, we take well-trained CBDNet, NBNet, HINet, Uformer and GMSNet into denoiser pool, and a U-Net is adopted to predict pixel-wise weighting maps to fuse these denoisers.
On Steering Multi-Annotations per Sample for Multi-Task Learning
Despite the remarkable progress, the challenge of optimally learning different tasks simultaneously remains to be explored.
Self-Supervised Learning for Real-World Super-Resolution from Dual Zoomed Observations
For this purpose, we take the telephoto image instead of an additional high-resolution image as the supervision information and select a center patch from it as the reference to super-resolve the corresponding short-focus image patch.
Crowd Counting via Perspective-Guided Fractional-Dilation Convolution
One of the main issues in this task is how to handle the dramatic scale variations of pedestrians caused by the perspective effect.
Flexible Image Denoising with Multi-layer Conditional Feature Modulation
For flexible non-blind image denoising, existing deep networks usually take both noisy image and noise level map as the input to handle various noise levels with a single model.
Multi-level Wavelet Convolutional Neural Networks
Specifically, MWCNN for image restoration is based on U-Net architecture, and inverse wavelet transform (IWT) is deployed to reconstruct the high resolution (HR) feature maps.
Multi-level Wavelet-CNN for Image Restoration
With the modified U-Net architecture, wavelet transform is introduced to reduce the size of feature maps in the contracting subnetwork.
Ranked #2 on
Grayscale Image Denoising
on Set12 sigma25
Metric Learning with Dynamically Generated Pairwise Constraints for Ear Recognition
Ear recognition task is known as predicting whether two ear images belong to the same person or not.
An Attention-Based Word-Level Interaction Model: Relation Detection for Knowledge Base Question Answering
Then, instead of merging the sequence into a single vector with pooling operation, soft alignments between words from the question and the relation are learned.
