9 citations · 14 across the 5 of their papers we have counts for
7 papers
Fine-grained length controllable video captioning with ordinal embeddings
Tomoya Nitta, Takumi Fukuzawa, Toru Tamaki
This paper proposes a method for video captioning that controls the length of generated captions. Previous work on length control often had few levels for expressing length. In thi…
Multi-model learning by sequential reading of untrimmed videos for action recognition
Kodai Kamiya, Toru Tamaki
We propose a new method for learning videos by aggregating multiple models by sequentially extracting video clips from untrimmed video. The proposed method reduces the correlation…
S3Aug: Segmentation, Sampling, and Shift for Action Recognition
Taiki Sugiura, Toru Tamaki
Action recognition is a well-established area of research in computer vision. In this paper, we propose S3Aug, a video data augmenatation for action recognition. Unlike conventiona…
Joint learning of images and videos with a single Vision Transformer
Shuki Shimizu, Toru Tamaki
In this study, we propose a method for jointly learning of images and videos using a single model. In general, images and videos are often trained by separate models. We propose 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…
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…