39 citations · 223 across the 46 of their papers we have counts for
49 papers
That's BAD: Blind Anomaly Detection by Implicit Local Feature Clustering
Jie Zhang, Masanori Suganuma, Takayuki Okatani
Recent studies on visual anomaly detection (AD) of industrial objects/textures have achieved quite good performance. They consider an unsupervised setting, specifically the one-cla…
Contextual Affinity Distillation for Image Anomaly Detection
Jie Zhang, Masanori Suganuma, Takayuki Okatani
Previous works on unsupervised industrial anomaly detection mainly focus on local structural anomalies such as cracks and color contamination. While achieving significantly high de…
Reference-based Motion Blur Removal: Learning to Utilize Sharpness in the Reference Image
Han Zou, Masanori Suganuma, Takayuki Okatani
Despite the recent advancement in the study of removing motion blur in an image, it is still hard to deal with strong blurs. While there are limits in removing blurs from a single…
RefVSR++: Exploiting Reference Inputs for Reference-based Video Super-resolution
Han Zou, Masanori Suganuma, Takayuki Okatani
Smartphones with multi-camera systems, featuring cameras with varying field-of-views (FoVs), are increasingly common. This variation in FoVs results in content differences across v…
Bridge Damage Cause Estimation Using Multiple Images Based on Visual Question Answering
Tatsuro Yamane, Pang-jo Chun, Ji Dang +1
In this paper, a bridge member damage cause estimation framework is proposed by calculating the image position using Structure from Motion (SfM) and acquiring its information via V…
SuperGF: Unifying Local and Global Features for Visual Localization
Wenzheng Song, Ran Yan, Boshu Lei +1
Advanced visual localization techniques encompass image retrieval challenges and 6 Degree-of-Freedom (DoF) camera pose estimation, such as hierarchical localization. Thus, they mus…
GRIT: Faster and Better Image captioning Transformer Using Dual Visual Features
Van-Quang Nguyen, Masanori Suganuma, Takayuki Okatani
Current state-of-the-art methods for image captioning employ region-based features, as they provide object-level information that is essential to describe the content of images; th…
Single-image Defocus Deblurring by Integration of Defocus Map Prediction Tracing the Inverse Problem Computation
Qian Ye, Masanori Suganuma, Takayuki Okatani
In this paper, we consider the problem in defocus image deblurring. Previous classical methods follow two-steps approaches, i.e., first defocus map estimation and then the non-blin…
Learning Regularized Multi-Scale Feature Flow for High Dynamic Range Imaging
Qian Ye, Masanori Suganuma, Jun Xiao +1
Reconstructing ghosting-free high dynamic range (HDR) images of dynamic scenes from a set of multi-exposure images is a challenging task, especially with large object motion and oc…
Rethinking Open-Set Object Detection: Issues, a New Formulation, and Taxonomy
Yusuke Hosoya, Masanori Suganuma, Takayuki Okatani
Open-set object detection (OSOD), a task involving the detection of unknown objects while accurately detecting known objects, has recently gained attention. However, we identify a…
Rethinking Unsupervised Domain Adaptation for Semantic Segmentation
Zhijie Wang, Masanori Suganuma, Takayuki Okatani
Unsupervised domain adaptation (UDA) adapts a model trained on one domain (called source) to a novel domain (called target) using only unlabeled data. Due to its high annotation co…
Improved Few-shot Segmentation by Redefinition of the Roles of Multi-level CNN Features
Zhijie Wang, Masanori Suganuma, Takayuki Okatani
This study is concerned with few-shot segmentation, i.e., segmenting the region of an unseen object class in a query image, given support image(s) of its instances. The current met…
Matching in the Dark: A Dataset for Matching Image Pairs of Low-light Scenes
Wenzheng Song, Masanori Suganuma, Xing Liu +3
This paper considers matching images of low-light scenes, aiming to widen the frontier of SfM and visual SLAM applications. Recent image sensors can record the brightness of scenes…
Cross-Region Domain Adaptation for Class-level Alignment
Zhijie Wang, Xing Liu, Masanori Suganuma +1
Semantic segmentation requires a lot of training data, which necessitates costly annotation. There have been many studies on unsupervised domain adaptation (UDA) from one domain to…
Progressive and Selective Fusion Network for High Dynamic Range Imaging
Qian Ye, Jun Xiao, Kin-man Lam +1
This paper considers the problem of generating an HDR image of a scene from its LDR images. Recent studies employ deep learning and solve the problem in an end-to-end fashion, lead…
Pushing the Envelope of Thin Crack Detection
Liang Xu, Taro Hatsutani, Xing Liu +3
In this study, we consider the problem of detecting cracks from the image of a concrete surface for automated inspection of infrastructure, such as bridges. Its overall accuracy is…
Bridging In- and Out-of-distribution Samples for Their Better Discriminability
Engkarat Techapanurak, Anh-Chuong Dang, Takayuki Okatani
This paper proposes a method for OOD detection. Questioning the premise of previous studies that ID and OOD samples are separated distinctly, we consider samples lying in the inter…
Practical Evaluation of Out-of-Distribution Detection Methods for Image Classification
Engkarat Techapanurak, Takayuki Okatani
We reconsider the evaluation of OOD detection methods for image recognition. Although many studies have been conducted so far to build better OOD detection methods, most of them fo…
How Can CNNs Use Image Position for Segmentation?
Rito Murase, Masanori Suganuma, Takayuki Okatani
Convolution is an equivariant operation, and image position does not affect its result. A recent study shows that the zero-padding employed in convolutional layers of CNNs provides…
Analysis of Deep Networks for Monocular Depth Estimation Through Adversarial Attacks with Proposal of a Defense Method
Junjie Hu, Takayuki Okatani
In this paper, we consider adversarial attacks against a system of monocular depth estimation (MDE) based on convolutional neural networks (CNNs). The motivation is two-fold. One i…
Efficient Attention Mechanism for Visual Dialog that can Handle All the Interactions between Multiple Inputs
Van-Quang Nguyen, Masanori Suganuma, Takayuki Okatani
It has been a primary concern in recent studies of vision and language tasks to design an effective attention mechanism dealing with interactions between the two modalities. The Tr…
Analysis and a Solution of Momentarily Missed Detection for Anchor-based Object Detectors
Yusuke Hosoya, Masanori Suganuma, Takayuki Okatani
The employment of convolutional neural networks has led to significant performance improvement on the task of object detection. However, when applying existing detectors to continu…
Restoring Images with Unknown Degradation Factors by Recurrent Use of a Multi-branch Network
Xing Liu, Masanori Suganuma, Xiyang Luo +1
The employment of convolutional neural networks has achieved unprecedented performance in the task of image restoration for a variety of degradation factors. However, high-performa…
Evaluating Artificial Systems for Pairwise Ranking Tasks Sensitive to Individual Differences
Xing Liu, Takayuki Okatani
Owing to the advancement of deep learning, artificial systems are now rival to humans in several pattern recognition tasks, such as visual recognition of object categories. However…