4,274 research outputs found
On utilizing weak estimators to achieve the online classification of data streams
Author's accepted version (post-print).Available from 03/09/2021.acceptedVersio
The Automatic Detection of Dataset Names in Scientific Articles
We study the task of recognizing named datasets in scientific articles as a Named Entity Recognition (NER) problem. Noticing that available annotated datasets were not adequate for our goals, we annotated 6000 sentences extracted from four major AI conferences, with roughly half of them containing one or more named datasets. A distinguishing feature of this set is the many sentences using enumerations, conjunctions and ellipses, resulting in long BI+ tag sequences. On all measures, the SciBERT NER tagger performed best and most robustly. Our baseline rule based tagger performed remarkably well and better than several state-of-the-art methods. The gold standard dataset, with links and offsets from each sentence to the (open access available) articles together with the annotation guidelines and all code used in the experiments, is available on GitHub
HyP-NeRF: Learning Improved NeRF Priors using a HyperNetwork
Neural Radiance Fields (NeRF) have become an increasingly popular
representation to capture high-quality appearance and shape of scenes and
objects. However, learning generalizable NeRF priors over categories of scenes
or objects has been challenging due to the high dimensionality of network
weight space. To address the limitations of existing work on generalization,
multi-view consistency and to improve quality, we propose HyP-NeRF, a latent
conditioning method for learning generalizable category-level NeRF priors using
hypernetworks. Rather than using hypernetworks to estimate only the weights of
a NeRF, we estimate both the weights and the multi-resolution hash encodings
resulting in significant quality gains. To improve quality even further, we
incorporate a denoise and finetune strategy that denoises images rendered from
NeRFs estimated by the hypernetwork and finetunes it while retaining multiview
consistency. These improvements enable us to use HyP-NeRF as a generalizable
prior for multiple downstream tasks including NeRF reconstruction from
single-view or cluttered scenes and text-to-NeRF. We provide qualitative
comparisons and evaluate HyP-NeRF on three tasks: generalization, compression,
and retrieval, demonstrating our state-of-the-art results
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