687 citations · 1k across the 28 of their papers we have counts for
33 papers
Neural Global Shutter: Learn to Restore Video from a Rolling Shutter Camera with Global Reset Feature
Zhixiang Wang, Xiang Ji, Jia-Bin Huang +3
Most computer vision systems assume distortion-free images as inputs. The widely used rolling-shutter (RS) image sensors, however, suffer from geometric distortion when the camera…
Optimal Correction Cost for Object Detection Evaluation
Mayu Otani, Riku Togashi, Yuta Nakashima +3
Mean Average Precision (mAP) is the primary evaluation measure for object detection. Although object detection has a broad range of applications, mAP evaluates detectors in terms o…
Improving Camouflaged Object Detection with the Uncertainty of Pseudo-edge Labels
Nobukatsu Kajiura, Hong Liu, Shin'ichi Satoh
This paper focuses on camouflaged object detection (COD), which is a task to detect objects hidden in the background. Most of the current COD models aim to highlight the target obj…
Scalable Personalised Item Ranking through Parametric Density Estimation
Riku Togashi, Masahiro Kato, Mayu Otani +2
Learning from implicit feedback is challenging because of the difficult nature of the one-class problem: we can observe only positive examples. Most conventional methods use a pair…
Density-Ratio Based Personalised Ranking from Implicit Feedback
Riku Togashi, Masahiro Kato, Mayu Otani +1
Learning from implicit user feedback is challenging as we can only observe positive samples but never access negative ones. Most conventional methods cope with this issue by adopti…
Image Inpainting Guided by Coherence Priors of Semantics and Textures
Liang Liao, Jing Xiao, Zheng Wang +2
Existing inpainting methods have achieved promising performance in recovering defected images of specific scenes. However, filling holes involving multiple semantic categories rema…
Alleviating Cold-Start Problems in Recommendation through Pseudo-Labelling over Knowledge Graph
Riku Togashi, Mayu Otani, Shin'ichi Satoh
Solving cold-start problems is indispensable to provide meaningful recommendation results for new users and items. Under sparsely observed data, unobserved user-item pairs are also…
Towards Unsupervised Crowd Counting via Regression-Detection Bi-knowledge Transfer
Yuting Liu, Zheng Wang, Miaojing Shi +3
Unsupervised crowd counting is a challenging yet not largely explored task. In this paper, we explore it in a transfer learning setting where we learn to detect and count persons i…
MADGAN: unsupervised Medical Anomaly Detection GAN using multiple adjacent brain MRI slice reconstruction
Changhee Han, Leonardo Rundo, Kohei Murao +7
Unsupervised learning can discover various unseen abnormalities, relying on large-scale unannotated medical images of healthy subjects. Towards this, unsupervised methods reconstru…
An Entropy Clustering Approach for Assessing Visual Question Difficulty
Kento Terao, Toru Tamaki, Bisser Raytchev +2
We propose a novel approach to identify the difficulty of visual questions for Visual Question Answering (VQA) without direct supervision or annotations to the difficulty. Prior wo…
Guidance and Evaluation: Semantic-Aware Image Inpainting for Mixed Scenes
Liang Liao, Jing Xiao, Zheng Wang +2
Completing a corrupted image with correct structures and reasonable textures for a mixed scene remains an elusive challenge. Since the missing hole in a mixed scene of a corrupted…
Bridging the gap between AI and Healthcare sides: towards developing clinically relevant AI-powered diagnosis systems
Changhee Han, Leonardo Rundo, Kohei Murao +2
Despite the success of Convolutional Neural Network-based Computer-Aided Diagnosis research, its clinical applications remain challenging. Accordingly, developing medical Artificia…
GAN-based Multiple Adjacent Brain MRI Slice Reconstruction for Unsupervised Alzheimer's Disease Diagnosis
Changhee Han, Leonardo Rundo, Kohei Murao +5
Unsupervised learning can discover various unseen diseases, relying on large-scale unannotated medical images of healthy subjects. Towards this, unsupervised methods reconstruct a…
Learning More with Less: GAN-based Medical Image Augmentation
Changhee Han, Kohei Murao, Shin'ichi Satoh +1
Convolutional Neural Network (CNN)-based accurate prediction typically requires large-scale annotated training data. In Medical Imaging, however, both obtaining medical data and an…
Beyond Intra-modality: A Survey of Heterogeneous Person Re-identification
Zheng Wang, Zhixiang Wang, Yinqiang Zheng +3
An efficient and effective person re-identification (ReID) system relieves the users from painful and boring video watching and accelerates the process of video analysis. Recently,…
DotSCN: Group Re-identification via Domain-Transferred Single and Couple Representation Learning
Ziling Huang, Zheng Wang, Chung-Chi Tsai +2
Group re-identification (G-ReID) is an important yet less-studied task. Its challenges not only lie in appearance changes of individuals which have been well-investigated in genera…
Illumination-Adaptive Person Re-identification
Zelong Zeng, Zhixiang Wang, Zheng Wang +3
Most person re-identification (ReID) approaches assume that person images are captured under relatively similar illumination conditions. In reality, long-term person retrieval is c…
Learning More with Less: Conditional PGGAN-based Data Augmentation for Brain Metastases Detection Using Highly-Rough Annotation on MR Images
Changhee Han, Kohei Murao, Tomoyuki Noguchi +5
Accurate Computer-Assisted Diagnosis, associated with proper data wrangling, can alleviate the risk of overlooking the diagnosis in a clinical environment. Towards this, as a Data…
Efficient Image Retrieval via Decoupling Diffusion into Online and Offline Processing
Fan Yang, Ryota Hinami, Yusuke Matsui +2
Diffusion is commonly used as a ranking or re-ranking method in retrieval tasks to achieve higher retrieval performance, and has attracted lots of attention in recent years. A down…
Reconfigurable Inverted Index
Yusuke Matsui, Ryota Hinami, Shin'ichi Satoh
Existing approximate nearest neighbor search systems suffer from two fundamental problems that are of practical importance but have not received sufficient attention from the resea…
Harnessing AI for Speech Reconstruction using Multi-view Silent Video Feed
Yaman Kumar, Mayank Aggarwal, Pratham Nawal +3
Speechreading or lipreading is the technique of understanding and getting phonetic features from a speaker's visual features such as movement of lips, face, teeth and tongue. It ha…
Multimodal Co-Training for Selecting Good Examples from Webly Labeled Video
Ryota Hinami, Junwei Liang, Shin'ichi Satoh +1
We tackle the problem of learning concept classifiers from videos on the web without using manually labeled data. Although metadata attached to videos (e.g., video titles, descript…
Tracked Instance Search
Andreu Girbau, Ryota Hinami, Shin'ichi Satoh
In this work we propose tracking as a generic addition to the instance search task. From video data perspective, much information that can be used is not taken into account in the…
Digital Watermarking for Deep Neural Networks
Yuki Nagai, Yusuke Uchida, Shigeyuki Sakazawa +1
Although deep neural networks have made tremendous progress in the area of multimedia representation, training neural models requires a large amount of data and time. It is well-kn…