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- Masao SakauchiSuggested from co-authorship
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Works508 from public data
- The SR-tree1,050
This paper proposes a new index structure called the SR-tree (Sphere/Rectangle-tree) which integrates bounding spheres and bounding rectangles which enhances the performance on nearest neighbor queries especially for high-dimensional and non-uniform data which can be practical in actual image/video similarity indexing.
- Embedding Watermarks into Deep Neural Networks812
This work proposes to use digital watermarking technology to protect intellectual property and detect intellectual property infringement in the use of trained models, and proposes a general framework for embedding a watermark in model parameters, using a parameter regularizer.
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- Name-It: naming and detecting faces in news videos316
Name-It, a system that associates faces and names in news videos, takes a multimodal video analysis approach: face sequence extraction and similarity evaluation from videos, name extraction from transcripts, and video-caption recognition.
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- Joint Detection and Recounting of Abnormal Events by Learning Deep Generic Knowledge278
This paper addresses the problem of joint detection and recounting of abnormal events in videos by integrating a generic CNN model and environment-dependent anomaly detectors and produces promising results of abnormal event recounting.
- Video OCR: indexing digital news libraries by recognition of superimposed captions211
To solve two problems of character recognition for videos, low-resolution characters and extremely complex backgrounds, an interpolation filter, multi-frame integration and character extraction filters are applied and the overall recognition results are satisfactory for use in news indexing.
- MADGAN: unsupervised medical anomaly detection GAN using multiple adjacent brain MRI slice reconstruction210
This work proposes unsupervised medical anomaly detection generative adversarial network (MADGAN), a novel two-step method using GAN-based multiple adjacent brain MRI slice reconstruction to detect brain anomalies at different stages on multi-sequence structural MRI.
- Physical Adversarial Attack Meets Computer Vision: A Decade Survey169
A comprehensive overview of physical adversarial attacks is presented and a proposed evaluation metric, hiPAA, comprises six perspectives: Effectiveness, Stealthiness, Robustness, Practicability, Aesthetics, and Economics.
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- Faster R-CNN Features for Instance Search153
This work explores the suitability for instance retrieval of image-and region-wise representations pooled from an object detection CNN such as Faster R-CNN and takes advantage of the object proposals learned by a Region Proposal Network (RPN) and their associated CNN features to build an instance search pipeline.
- Guidance and Evaluation: Semantic-Aware Image Inpainting for Mixed Scenes139
This paper proposes a Semantic Guidance and Evaluation Network (SGE-Net) to iteratively update the structural priors and the inpainted image in an interplay framework of semantics extraction and image inpainting, which utilizes semantic segmentation map as guidance in each scale of inPainting.
- Cascaded SR-GAN for Scale-Adaptive Low Resolution Person Re-identification123
A unified formulation, a Cascaded Super-Resolution GAN (CSR-GAN) framework is proposed, which requires a network not only synthesizing high-resolution images with different upscaling factors, but also extracting discriminative image feature for judging person’s identity.
- Image Inpainting Guided by Coherence Priors of Semantics and Textures118
A multi-scale joint optimization framework is adopted to first model the coherence priors and then accordingly interleaving optimize image inpainting and semantic segmentation in a coarse-to-fine manner and two coherence losses are proposed to constrain the consistency between the semantics and the inpainted image in terms of the overall structure and detailed textures.
- Cooking navi117
According to the result of a preliminary experiment, all users from novice to experienced cooks could finish two dishes in parallel while enjoyeing the cooking very much, shows the effectiveness of the multimedia navigation that is proposed.
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- Learning More with Less102
This paper proposes Conditional Progressive Growing of GANs (CPGGANs), incorporating highly-rough bounding box conditions incrementally into PGGANs to place brain metastases at desired positions/sizes on 256 × 256 Magnetic Resonance (MR) images, for Convolutional Neural Network-based tumor detection; this first GAN-based medical DA using automatic boundingbox annotation improves the training robustness.
- Learning More with Less: Conditional PGGAN-based Data Augmentation for Brain Metastases Detection Using Highly-Rough Annotation on MR Images102
This paper proposes Conditional Progressive Growing of GANs (CPGGANs), incorporating highly-rough bounding box conditions incrementally into PGGANs to place brain metastases at desired positions/sizes on 256 × 256 Magnetic Resonance (MR) images, for Convolutional Neural Network-based tumor detection; this first GAN-based medical DA using automatic boundingbox annotation improves the training robustness.
- Building Compact Local Pairwise Codebook with Joint Feature Space Clustering100
Experimental results on challenging datasets demonstrate that LPC outperforms the baselines and performs competitively against the state-of-the-art techniques in scene and object categorization tasks where a large number of categories need to be recognized.
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- Evaluation of deep convolutional neural networks for glaucoma detection87
DCNNs may be a useful tool for detecting glaucoma or glauca-suspected eyes by use of fundus color images and proper preprocessing and collection of qualified images are essential to improving the discriminative ability.
- Learning Sparse and Identity-Preserved Hidden Attributes for Person Re-Identification82
This work proposes to incrementally generate Deep Hidden Attribute (DHA) based on baseline deep network for newly uncovered annotations based on an auto-encoder model that can be plugged into any deep network to mine latent information in an unsupervised manner.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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