Jordi Pont-Tuset bio photo

Jordi Pont-Tuset

I'm from a small town near Girona, Catalunya. Currently in Zürich, Switzerland. Passionate about computer vision, technology, running, and mountains.

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Just after CVPR 2015, I described the exponential growth that deep learning had in vision paper (see the original blog post) by analyzing the paper titles.

Since the CVF just released the papers here, I updated the script and run it. You can also find all papers in BibTex format in this folder.

Here the result in the command line:

> python
CVPR2013: 0.85% ( 4 out of 471)
ICCV2013: 1.54% ( 7 out of 455)
CVPR2014: 3.70% (20 out of 540)
CVPR2015: 14.45% (87 out of 602)
ICCV2015: 14.45% (76 out of 526)

And the plot:


Surprisingly, the percentage of papers with some of the deep-learning keywords is the same for ICCV 2015 than for CVPR 2015. Does this mean that deep learning has plateaued in computer vision?

I feel that the general opinion will be that the field is still far from reaching a plateau. My interpretation for the titles not increasing is that deep learning is almost taken for granted in some sub-fields, so some authors do not feel the need to specify it in the title.

Here the updated code for you to try:

#!/usr/bin/env python
# -*- coding: utf-8 -*-

import requests
from lxml import html

conferences = ["CVPR2013","ICCV2013","CVPR2014","CVPR2015","ICCV2015"]

for conf in conferences:
# Get the HTML text and find the classes of type 'ptitle'
response = requests.get(""+conf+".py")
tree = html.fromstring(response.text)
papers = tree.find_class('ptitle')

# Get all titles in a list
all_titles = []
for paper in papers:
title = paper.xpath('a/text()')

# Search for the 'deep'-inducing keywords
keywords = ['deep', 'cnn', 'convolutional', 'neural network']
count = 0
for title in all_titles:
for kword in keywords:
if title.lower().find(kword)>=0:
count = count+1
percent = count/float(len(all_titles))*100
print("%s: %.2f%% (%d out of %d)" % (conf, percent, count, len(all_titles)))