activity
20152021
most citedMILJS : Brand New JavaScript Libraries for Matrix Calculation and Machine Learning

1 citations · 1 across the 1 of their papers we have counts for

collaborators

5 papers

cs.CV2021

Hierarchical Lovász Embeddings for Proposal-free Panoptic Segmentation

Tommi Kerola, Jie Li, Atsushi Kanehira +3

Panoptic segmentation brings together two separate tasks: instance and semantic segmentation. Although they are related, unifying them faces an apparent paradox: how to learn simul…

cs.CV2018

Learning to Explain with Complemental Examples

Atsushi Kanehira, Tatsuya Harada

This paper addresses the generation of explanations with visual examples. Given an input sample, we build a system that not only classifies it to a specific category, but also outp…

cs.CV2018

Multimodal Explanations by Predicting Counterfactuality in Videos

Atsushi Kanehira, Kentaro Takemoto, Sho Inayoshi +1

This study addresses generating counterfactual explanations with multimodal information. Our goal is not only to classify a video into a specific category, but also to provide expl…

cs.CV2018

Viewpoint-aware Video Summarization

Atsushi Kanehira, Luc Van Gool, Yoshitaka Ushiku +1

This paper introduces a novel variant of video summarization, namely building a summary that depends on the particular aspect of a video the viewer focuses on. We refer to this as…

stat.ML20151 cited

MILJS : Brand New JavaScript Libraries for Matrix Calculation and Machine Learning

Ken Miura, Tetsuaki Mano, Atsushi Kanehira +2

MILJS is a collection of state-of-the-art, platform-independent, scalable, fast JavaScript libraries for matrix calculation and machine learning. Our core library offering a matrix…