activity
20162019
most citedConsensus-based Sequence Training for Video Captioning

18 citations · 29 across the 4 of their papers we have counts for

collaborators

6 papers

cs.CV201911 cited

Learning More with Less: GAN-based Medical Image Augmentation

Changhee Han, Kohei Murao, Shin'ichi Satoh +1

Convolutional Neural Network (CNN)-based accurate prediction typically requires large-scale annotated training data. In Medical Imaging, however, both obtaining medical data and an…

cs.CV201718 cited

Consensus-based Sequence Training for Video Captioning

Sang Phan, Gustav Eje Henter, Yusuke Miyao +1

Captioning models are typically trained using the cross-entropy loss. However, their performance is evaluated on other metrics designed to better correlate with human assessments.…

cs.CV2017

Joint Detection and Recounting of Abnormal Events by Learning Deep Generic Knowledge

Ryota Hinami, Tao Mei, Shin'ichi Satoh

This paper addresses the problem of joint detection and recounting of abnormal events in videos. Recounting of abnormal events, i.e., explaining why they are judged to be abnormal,…

cs.MM2017

Region-Based Image Retrieval Revisited

Ryota Hinami, Yusuke Matsui, Shin'ichi Satoh

Region-based image retrieval (RBIR) technique is revisited. In early attempts at RBIR in the late 90s, researchers found many ways to specify region-based queries and spatial relat…

cs.CV2017

Active Learning for Structured Prediction from Partially Labeled Data

Mehran Khodabandeh, Zhiwei Deng, Mostafa S. Ibrahim +2

We propose a general purpose active learning algorithm for structured prediction, gathering labeled data for training a model that outputs a set of related labels for an image or v…

cs.CV2016

Faster R-CNN Features for Instance Search

Amaia Salvador, Xavier Giro-i-Nieto, Ferran Marques +1

Image representations derived from pre-trained Convolutional Neural Networks (CNNs) have become the new state of the art in computer vision tasks such as instance retrieval. This w…