17 citations · 40 across the 7 of their papers we have counts for
7 papers · 1 filter
Pre-training without Natural Images
Hirokatsu Kataoka, Kazushige Okayasu, Asato Matsumoto +5
Is it possible to use convolutional neural networks pre-trained without any natural images to assist natural image understanding? The paper proposes a novel concept, Formula-driven…
Neural Joking Machine : Humorous image captioning
Kota Yoshida, Munetaka Minoguchi, Kenichiro Wani +2
What is an effective expression that draws laughter from human beings? In the present paper, in order to consider this question from an academic standpoint, we generate an image ca…
Collaborative Descriptors: Convolutional Maps for Preprocessing
Hirokatsu Kataoka, Kaori Abe, Akio Nakamura +1
The paper presents a novel concept for collaborative descriptors between deeply learned and hand-crafted features. To achieve this concept, we apply convolutional maps for pre-proc…
Could you guess an interesting movie from the posters?: An evaluation of vision-based features on movie poster database
Yuta Matsuzaki, Kazushige Okayasu, Takaaki Imanari +5
In this paper, we aim to estimate the Winner of world-wide film festival from the exhibited movie poster. The task is an extremely challenging because the estimation must be done w…
Changing Fashion Cultures
Kaori Abe, Teppei Suzuki, Shunya Ueta +3
The paper presents a novel concept that analyzes and visualizes worldwide fashion trends. Our goal is to reveal cutting-edge fashion trends without displaying an ordinary fashion s…
cvpaper.challenge in 2015 - A review of CVPR2015 and DeepSurvey
Hirokatsu Kataoka, Yudai Miyashita, Tomoaki Yamabe +9
The "cvpaper.challenge" is a group composed of members from AIST, Tokyo Denki Univ. (TDU), and Univ. of Tsukuba that aims to systematically summarize papers on computer vision, pat…
Semantic Change Detection with Hypermaps
Teppei Suzuki, Soma Shirakabe, Yudai Miyashita +3
Change detection is the study of detecting changes between two different images of a scene taken at different times. By the detected change areas, however, a human cannot understan…