34 citations · 102 across the 29 of their papers we have counts for
31 papers
EviSplat: Preserving Multi-View Evidence in 3D Gaussian Splatting for Open-Vocabulary Segmentation
Sungho Moon, Kota Shimomura, Junwoo Park +4
Open-vocabulary 3D scene understanding enables object localization and segmentation from free-form text queries without a fixed category vocabulary. Many recent methods build on 3D…
Structured Pruning of Large Language Models via Power Transformation and Sign-Preserving Score Aggregation with Adaptive Feature Retention
Ryota Kobayashi, Tsubasa Hirakawa, Takayoshi Yamashita +4
This paper proposes an improved structured pruning method for large language models (LLMs) that addresses key challenges in adapting Adaptive Feature Retention (AFR), an unstructur…
P2GS: Physical Prior-guided Gaussian Splatting for Photometrically Consistent Urban Reconstruction
Kota Shimomura, Hidehisa Arai, Tsubasa Takahashi +2
3D Gaussian Splatting (3DGS) has recently emerged as a powerful explicit representation enabling fast, high-fidelity rendering, making it a promising foundation for closed-loop sim…
SunnyParking: Multi-Shot Trajectory Generation and Motion State Awareness for Human-like Parking
Jishu Miao, Han Chen, Jiankun Zhai +4
Autonomous parking fundamentally differs from on-road driving due to its frequent direction changes and complex maneuvering requirements. However, existing End-to-End (E2E) plannin…
Mix-Geneformer: Unified Representation Learning for Human and Mouse scRNA-seq Data
Yuki Nishio, Takayoshi Yamashita, Keita Ito +2
Single-cell RNA sequencing (scRNA-seq) enables single-cell transcriptomic profiling, revealing cellular heterogeneity and rare populations. Recent deep learning models like Genefor…
DeBiFormer: Vision Transformer with Deformable Agent Bi-level Routing Attention
Nguyen Huu Bao Long, Chenyu Zhang, Yuzhi Shi +4
Vision Transformers with various attention modules have demonstrated superior performance on vision tasks. While using sparsity-adaptive attention, such as in DAT, has yielded stro…
Nearest Neighbor Future Captioning: Generating Descriptions for Possible Collisions in Object Placement Tasks
Takumi Komatsu, Motonari Kambara, Shumpei Hatanaka +5
Domestic service robots (DSRs) that support people in everyday environments have been widely investigated. However, their ability to predict and describe future risks resulting fro…
Layer-Wise Relevance Propagation with Conservation Property for ResNet
Seitaro Otsuki, Tsumugi Iida, Félix Doublet +4
The transparent formulation of explanation methods is essential for elucidating the predictions of neural networks, which are typically black-box models. Layer-wise Relevance Propa…
Action Q-Transformer: Visual Explanation in Deep Reinforcement Learning with Encoder-Decoder Model using Action Query
Hidenori Itaya, Tsubasa Hirakawa, Takayoshi Yamashita +2
The excellent performance of Transformer in supervised learning has led to growing interest in its potential application to deep reinforcement learning (DRL) to achieve high perfor…
Learning from AI: An Interactive Learning Method Using a DNN Model Incorporating Expert Knowledge as a Teacher
Kohei Hattori, Tsubasa Hirakawa, Takayoshi Yamashita +1
Visual explanation is an approach for visualizing the grounds of judgment by deep learning, and it is possible to visually interpret the grounds of a judgment for a certain input b…
PALF: Pre-Annotation and Camera-LiDAR Late Fusion for the Easy Annotation of Point Clouds
Yucheng Zhang, Masaki Fukuda, Yasunori Ishii +2
3D object detection has become indispensable in the field of autonomous driving. To date, gratifying breakthroughs have been recorded in 3D object detection research, attributed to…
Deep Single Image Camera Calibration by Heatmap Regression to Recover Fisheye Images Under Manhattan World Assumption
Nobuhiko Wakai, Satoshi Sato, Yasunori Ishii +1
A Manhattan world lying along cuboid buildings is useful for camera angle estimation. However, accurate and robust angle estimation from fisheye images in the Manhattan world has r…
Masking and Mixing Adversarial Training
Hiroki Adachi, Tsubasa Hirakawa, Takayoshi Yamashita +3
While convolutional neural networks (CNNs) have achieved excellent performances in various computer vision tasks, they often misclassify with malicious samples, a.k.a. adversarial…
Data Augmentation by Selecting Mixed Classes Considering Distance Between Classes
Shungo Fujii, Yasunori Ishii, Kazuki Kozuka +3
Data augmentation is an essential technique for improving recognition accuracy in object recognition using deep learning. Methods that generate mixed data from multiple data sets,…
Few-shot Adaptive Object Detection with Cross-Domain CutMix
Yuzuru Nakamura, Yasunori Ishii, Yuki Maruyama +1
In object detection, data amount and cost are a trade-off, and collecting a large amount of data in a specific domain is labor intensive. Therefore, existing large-scale datasets a…
Visual Explanation of Deep Q-Network for Robot Navigation by Fine-tuning Attention Branch
Yuya Maruyama, Hiroshi Fukui, Tsubasa Hirakawa +3
Robot navigation with deep reinforcement learning (RL) achieves higher performance and performs well under complex environment. Meanwhile, the interpretation of the decision-making…
Invisible-to-Visible: Privacy-Aware Human Segmentation using Airborne Ultrasound via Collaborative Learning Probabilistic U-Net
Risako Tanigawa, Yasunori Ishii, Kazuki Kozuka +1
Color images are easy to understand visually and can acquire a great deal of information, such as color and texture. They are highly and widely used in tasks such as segmentation.…
Invisible-to-Visible: Privacy-Aware Human Instance Segmentation using Airborne Ultrasound via Collaborative Learning Variational Autoencoder
Risako Tanigawa, Yasunori Ishii, Kazuki Kozuka +1
In action understanding in indoor, we have to recognize human pose and action considering privacy. Although camera images can be used for highly accurate human action recognition,…
Rethinking Generic Camera Models for Deep Single Image Camera Calibration to Recover Rotation and Fisheye Distortion
Nobuhiko Wakai, Satoshi Sato, Yasunori Ishii +1
Although recent learning-based calibration methods can predict extrinsic and intrinsic camera parameters from a single image, the accuracy of these methods is degraded in fisheye i…
ST-ABN: Visual Explanation Taking into Account Spatio-temporal Information for Video Recognition
Masahiro Mitsuhara, Tsubasa Hirakawa, Takayoshi Yamashita +1
It is difficult for people to interpret the decision-making in the inference process of deep neural networks. Visual explanation is one method for interpreting the decision-making…
CutDepth:Edge-aware Data Augmentation in Depth Estimation
Yasunori Ishii, Takayoshi Yamashita
It is difficult to collect data on a large scale in a monocular depth estimation because the task requires the simultaneous acquisition of RGB images and depths. Data augmentation…
Deep Ensemble Collaborative Learning by using Knowledge-transfer Graph for Fine-grained Object Classification
Naoki Okamoto, Soma Minami, Tsubasa Hirakawa +2
Mutual learning, in which multiple networks learn by sharing their knowledge, improves the performance of each network. However, the performance of ensembles of networks that have…
Visual Explanation using Attention Mechanism in Actor-Critic-based Deep Reinforcement Learning
Hidenori Itaya, Tsubasa Hirakawa, Takayoshi Yamashita +2
Deep reinforcement learning (DRL) has great potential for acquiring the optimal action in complex environments such as games and robot control. However, it is difficult to analyze…
Predicting and Attending to Damaging Collisions for Placing Everyday Objects in Photo-Realistic Simulations
Aly Magassouba, Komei Sugiura, Angelica Nakayama +4
Placing objects is a fundamental task for domestic service robots (DSRs). Thus, inferring the collision-risk before a placing motion is crucial for achieving the requested task. Th…