Findings from published research, checked in the open

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1,428 claims from 888 papers are on the record. 46 have been checked so far; the other 1,382 have no check with a result yet.

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Keyword: object localization ×Clear all

4 claims from 2 papers

1. Computer Science › Advanced Neural Network Applications

   Very Deep Convolutional Networks for Large-Scale Image Recognition

   Simonyan and Zisserman · arXiv (Cornell University) · 2014

   The paper tests how network depth affects accuracy in large-scale image recognition, finding that very small 3x3 filters with 16-19 weight layers improve markedly on earlier configurations.

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   1. UncheckedThe paper states that image representations learned by its very deep networks generalise well to other datasets, reaching state-of-the-art results there.“We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results.”
   2. UncheckedThe authors say their deeper-network findings underpinned their ImageNet 2014 entry, which placed first in localisation and second in classification.“These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively.”

2. Computer Science › Advanced Neural Network Applications

   A Random CNN Sees Objects: One Inductive Bias of CNN and Its Applications

   Cao and Wu · arXiv (Cornell University) · 2021

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   1. Unchecked“Experimental results show that the proposed Tobias significantly improves downstream tasks, especially for object detection.”
   2. Unchecked“This paper also shows that Tobias has consistent improvements on training sets of different sizes, and is more resilient to changes in image augmentations.”