activity
20172026
most citedSelf-Supervised Difference Detection for Weakly-Supervised Semantic Segmentation

14 citations · 54 across the 8 of their papers we have counts for

collaborators

8 papers

cs.CV2026

Efficient Spatial-Temporal Focal Adapter with SSM for Temporal Action Detection

Yicheng Qiu, Keiji Yanai

Temporal human action detection aims to identify and localize action segments within untrimmed videos, serving as a pivotal task in video understanding. Despite the progress achiev…

cs.CV2022

Transformer-based Cross-Modal Recipe Embeddings with Large Batch Training

Jing Yang, Junwen Chen, Keiji Yanai

In this paper, we present a cross-modal recipe retrieval framework, Transformer-based Network for Large Batch Training (TNLBT), which is inspired by ACME~(Adversarial Cross-Modal E…

cs.CV2021★ 9 cited

QAHOI: Query-Based Anchors for Human-Object Interaction Detection

Junwen Chen, Keiji Yanai

Human-object interaction (HOI) detection as a downstream of object detection tasks requires localizing pairs of humans and objects and extracting the semantic relationships between…

cs.CV2020★ 13 cited

IPN Hand: A Video Dataset and Benchmark for Real-Time Continuous Hand Gesture Recognition

Gibran Benitez-Garcia, Jesus Olivares-Mercado, Gabriel Sanchez-Perez +1

In this paper, we introduce a new benchmark dataset named IPN Hand with sufficient size, variety, and real-world elements able to train and evaluate deep neural networks. This data…

cs.GR2020★ 10 cited

Iconify: Converting Photographs into Icons

Takuro Karamatsu, Gibran Benitez-Garcia, Keiji Yanai +1

In this paper, we tackle a challenging domain conversion task between photo and icon images. Although icons often originate from real object images (i.e., photographs), severe abst…

cs.CV2019★ 14 cited

Self-Supervised Difference Detection for Weakly-Supervised Semantic Segmentation

Wataru Shimoda, Keiji Yanai

To minimize the annotation costs associated with the training of semantic segmentation models, researchers have extensively investigated weakly-supervised segmentation approaches.…

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

Scene Text Eraser

Toshiki Nakamura, Anna Zhu, Keiji Yanai +1

The character information in natural scene images contains various personal information, such as telephone numbers, home addresses, etc. It is a high risk of leakage the informatio…