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Found 6 papers, 1 papers with code
ParaHome: Parameterizing Everyday Home Activities Towards 3D Generative Modeling of Human-Object Interactions
To address this challenge, we introduce the ParaHome system, designed to capture and parameterize dynamic 3D movements of humans and objects within a common home environment.
Constructing Vec-tionaries to Extract Message Features from Texts: A Case Study of Moral Appeals
Using moral content in tweets as a case study, we illustrate the steps to construct the moral foundations vec-tionary, showcasing its ability to process texts missed by conventional dictionaries and word embedding methods and to produce measurements better aligned with crowdsourced human assessments.
Retrieving Evidence from EHRs with LLMs: Possibilities and Challenges
Our method entails tasking an LLM to infer whether a patient has, or is at risk of, a particular condition on the basis of associated notes; if so, we ask the model to summarize the supporting evidence.
TextManiA: Enriching Visual Feature by Text-driven Manifold Augmentation
Given the hypothesis, TextManiA transfers pre-trained text representation obtained from a well-established large language encoder to a target visual feature space being learned.
Multi-Temporal Recurrent Neural Networks For Progressive Non-Uniform Single Image Deblurring With Incremental Temporal Training
Multi-scale (MS) approaches have been widely investigated for blind single image / video deblurring that sequentially recovers deblurred images in low spatial scale first and then in high spatial scale later with the output of lower scales.
Ranked #14 on
Deblurring
on HIDE (trained on GOPRO)
Down-Scaling with Learned Kernels in Multi-Scale Deep Neural Networks for Non-Uniform Single Image Deblurring
Our proposed CNN-based down-scaling was the key factor for this excellent performance since the performance of our network without it was decreased by 1. 98dB.
