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Found 7 papers, 5 papers with code
Benchmarking LLMs via Uncertainty Quantification
The proliferation of open-source Large Language Models (LLMs) from various institutions has highlighted the urgent need for comprehensive evaluation methods.
Context Consistency between Training and Testing in Simultaneous Machine Translation
Simultaneous Machine Translation (SiMT) aims to yield a real-time partial translation with a monotonically growing the source-side context.
Rethinking Word-Level Auto-Completion in Computer-Aided Translation
Word-Level Auto-Completion (WLAC) plays a crucial role in Computer-Assisted Translation.
Image super-resolution via dynamic network
In this paper, we present a dynamic network for image super-resolution (DSRNet), which contains a residual enhancement block, wide enhancement block, feature refinement block and construction block.
Ranked #49 on
Image Super-Resolution
on Set14 - 4x upscaling
Hard Exudate Segmentation Supplemented by Super-Resolution with Multi-scale Attention Fusion Module
Specifically, we propose a fusion module named Multi-scale Attention Fusion (MAF) module for our dual-stream framework to effectively integrate features of the two tasks.
Dual-path CNN with Max Gated block for Text-Based Person Re-identification
With that in mind, a novel Dual-path CNN with Max Gated block (DCMG) is proposed to extract discriminative word embeddings and make visual-textual association concern more on remarkable features of both modalities.
Sentence-Level Agreement for Neural Machine Translation
The training objective of neural machine translation (NMT) is to minimize the loss between the words in the translated sentences and those in the references.
