activity
20152023
most citedUnsupervised Learning of Image Segmentation Based on Differentiable Feature Clustering

253 citations · 368 across the 17 of their papers we have counts for

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Showing cs.CVShow all

9 papers · 1 filter

cs.CV2023★ 3 cited

Cross-Level Distillation and Feature Denoising for Cross-Domain Few-Shot Classification

Hao Zheng, Runqi Wang, Jianzhuang Liu +1

The conventional few-shot classification aims at learning a model on a large labeled base dataset and rapidly adapting to a target dataset that is from the same distribution as the…

cs.CV2023

Multi-goal Audio-visual Navigation using Sound Direction Map

Haru Kondoh, Asako Kanezaki

Over the past few years, there has been a great deal of research on navigation tasks in indoor environments using deep reinforcement learning agents. Most of these tasks use only v…

cs.CV2022★ 2 cited

Object Memory Transformer for Object Goal Navigation

Rui Fukushima, Kei Ota, Asako Kanezaki +2

This paper presents a reinforcement learning method for object goal navigation (ObjNav) where an agent navigates in 3D indoor environments to reach a target object based on long-te…

cs.CV2020★ 253 cited

Unsupervised Learning of Image Segmentation Based on Differentiable Feature Clustering

Wonjik Kim, Asako Kanezaki, Masayuki Tanaka

The usage of convolutional neural networks (CNNs) for unsupervised image segmentation was investigated in this study. In the proposed approach, label prediction and network paramet…

cs.CV2019

Salient object detection on hyperspectral images using features learned from unsupervised segmentation task

Nevrez Imamoglu, Guanqun Ding, Yuming Fang +3

Various saliency detection algorithms from color images have been proposed to mimic eye fixation or attentive object detection response of human observers for the same scenes. Howe…

cs.CV2018★ 8 cited

An Integration of Bottom-up and Top-Down Salient Cues on RGB-D Data: Saliency from Objectness vs. Non-Objectness

Nevrez Imamoglu, Wataru Shimoda, Chi Zhang +4

Bottom-up and top-down visual cues are two types of information that helps the visual saliency models. These salient cues can be from spatial distributions of the features (space-b…

cs.CV2017

cvpaper.challenge in 2016: Futuristic Computer Vision through 1,600 Papers Survey

Hirokatsu Kataoka, Soma Shirakabe, Yun He +14

The paper gives futuristic challenges disscussed in the cvpaper.challenge. In 2015 and 2016, we thoroughly study 1,600+ papers in several conferences/journals such as CVPR/ICCV/ECC…

cs.CV2016★ 50 cited

RotationNet: Joint Object Categorization and Pose Estimation Using Multiviews from Unsupervised Viewpoints

Asako Kanezaki, Yasuyuki Matsushita, Yoshifumi Nishida

We propose a Convolutional Neural Network (CNN)-based model "RotationNet," which takes multi-view images of an object as input and jointly estimates its pose and object category. U…

cs.CV2015★ 7 cited

Recognizing Activities of Daily Living with a Wrist-mounted Camera

Katsunori Ohnishi, Atsushi Kanehira, Asako Kanezaki +1

We present a novel dataset and a novel algorithm for recognizing activities of daily living (ADL) from a first-person wearable camera. Handled objects are crucially important for e…