34 citations · 89 across the 25 of their papers we have counts for
17 papers · 1 filter
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…
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…
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,…
Object-ABN: Learning to Generate Sharp Attention Maps for Action Recognition
Tomoya Nitta, Tsubasa Hirakawa, Hironobu Fujiyoshi +1
In this paper we propose an extension of the Attention Branch Network (ABN) by using instance segmentation for generating sharper attention maps for action recognition. Methods for…
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…
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…
Alleviating the Burden of Labeling: Sentence Generation by Attention Branch Encoder-Decoder Network
Tadashi Ogura, Aly Magassouba, Komei Sugiura +4
Domestic service robots (DSRs) are a promising solution to the shortage of home care workers. However, one of the main limitations of DSRs is their inability to interact naturally…
Improved Activity Forecasting for Generating Trajectories
Daisuke Ogawa, Toru Tamaki, Tsubasa Hirakawa +3
An efficient inverse reinforcement learning for generating trajectories is proposed based of 2D and 3D activity forecasting. We modify reward function with norm and propose c…
Knowledge Transfer Graph for Deep Collaborative Learning
Soma Minami, Tsubasa Hirakawa, Takayoshi Yamashita +1
Knowledge transfer among multiple networks using their outputs or intermediate activations have evolved through extensive manual design from a simple teacher-student approach (know…
Embedding Human Knowledge into Deep Neural Network via Attention Map
Masahiro Mitsuhara, Hiroshi Fukui, Yusuke Sakashita +4
In this work, we aim to realize a method for embedding human knowledge into deep neural networks. While the conventional method to embed human knowledge has been applied for non-de…
Attention Branch Network: Learning of Attention Mechanism for Visual Explanation
Hiroshi Fukui, Tsubasa Hirakawa, Takayoshi Yamashita +1
Visual explanation enables human to understand the decision making of Deep Convolutional Neural Network (CNN), but it is insufficient to contribute the performance improvement. In…
Survey on Vision-based Path Prediction
Tsubasa Hirakawa, Takayoshi Yamashita, Toru Tamaki +1
Path prediction is a fundamental task for estimating how pedestrians or vehicles are going to move in a scene. Because path prediction as a task of computer vision uses video as in…
Development of a Real-time Colorectal Tumor Classification System for Narrow-band Imaging zoom-videoendoscopy
Tsubasa Hirakawa, Toru Tamaki, Bisser Raytchev +5
Colorectal endoscopy is important for the early detection and treatment of colorectal cancer and is used worldwide. A computer-aided diagnosis (CAD) system that provides an objecti…
Domain Adaptation with L2 constraints for classifying images from different endoscope systems
Toru Tamaki, Shoji Sonoyama, Takio Kurita +8
This paper proposes a method for domain adaptation that extends the maximum margin domain transfer (MMDT) proposed by Hoffman et al., by introducing L2 distance constraints between…
Transfer Learning for Endoscopic Image Classification
Shoji Sonoyama, Toru Tamaki, Tsubasa Hirakawa +6
In this paper we propose a method for transfer learning of endoscopic images. For transferring between features obtained from images taken by different (old and new) endoscopes, we…
Computer-Aided Colorectal Tumor Classification in NBI Endoscopy Using CNN Features
Toru Tamaki, Shoji Sonoyama, Tsubasa Hirakawa +6
In this paper we report results for recognizing colorectal NBI endoscopic images by using features extracted from convolutional neural network (CNN). In this comparative study, we…