Search Results for author:
Found 61 papers, 20 papers with code
Temporal Insight Enhancement: Mitigating Temporal Hallucination in Multimodal Large Language Models
This study introduces an innovative method to address event-level hallucinations in MLLMs, focusing on specific temporal understanding in video content.
SBCFormer: Lightweight Network Capable of Full-size ImageNet Classification at 1 FPS on Single Board Computers
For the first time, it achieves an ImageNet-1K top-1 accuracy of around 80% at a speed of 1. 0 frame/sec on the SBC.
Visual Abductive Reasoning Meets Driving Hazard Prediction
To enable research in this understudied area, a new dataset named the DHPR (Driving Hazard Prediction and Reasoning) dataset is created.
That's BAD: Blind Anomaly Detection by Implicit Local Feature Clustering
They consider an unsupervised setting, specifically the one-class setting, in which we assume the availability of a set of normal (\textit{i. e.}, anomaly-free) images for training.
Reference-based Motion Blur Removal: Learning to Utilize Sharpness in the Reference Image
We can utilize an alternative shot of the identical scene, just like in video deblurring, or we can even employ a distinct image from another scene.
RefVSR++: Exploiting Reference Inputs for Reference-based Video Super-resolution
Then, we propose an improved method, RefVSR++, which can aggregate two features in parallel in the temporal direction, one for aggregating the fused LR and Ref inputs and the other for Ref inputs over time.
Reference-based Video Super-Resolution
Video Super-Resolution
Contextual Affinity Distillation for Image Anomaly Detection
The local student, which is used in previous studies mainly focuses on structural anomaly detection while the global student pays attention to logical anomalies.
Ranked #13 on
Anomaly Detection
on MVTec LOCO AD
Bridge Damage Cause Estimation Using Multiple Images Based on Visual Question Answering
For this, a VQA model was developed that uses bridge images for dataset creation and outputs the damage or member name and its existence based on the images and questions.
SuperGF: Unifying Local and Global Features for Visual Localization
In this study, we present a novel method called SuperGF, which effectively unifies local and global features for visual localization, leading to a higher trade-off between localization accuracy and computational efficiency.
GRIT: Faster and Better Image captioning Transformer Using Dual Visual Features
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; they are usually extracted by an object detector such as Faster R-CNN.
Ranked #8 on
Image Captioning
on nocaps in-domain
Rectifying Open-set Object Detection: A Taxonomy, Practical Applications, and Proper Evaluation
In this paper, we first point out that the recent studies' formalization of OSOD, which generalizes open-set recognition (OSR) and thus considers an unlimited variety of unknown objects, has a fundamental issue.
Single-image Defocus Deblurring by Integration of Defocus Map Prediction Tracing the Inverse Problem Computation
Considering the spatial variant property of the defocus blur and the blur level indicated in the defocus map, we employ the defocus map as conditional guidance to adjust the features from the input blurring images instead of simple concatenation.
Learning Regularized Multi-Scale Feature Flow for High Dynamic Range Imaging
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 occlusions, leading to visible artifacts using existing methods.
Rethinking Unsupervised Domain Adaptation for Semantic Segmentation
Due to its high annotation cost, researchers have developed many UDA methods for semantic segmentation, which assume no labeled sample is available in the target domain.
Symmetry-Aware Neural Architecture for Embodied Visual Exploration
We then propose a network design for the actor and the critic to inherently attain these symmetries.
Symmetry-aware Neural Architecture for Embodied Visual Navigation
We then propose a network design for the actor and the critic to inherently attain these symmetries.
Improved Few-shot Segmentation by Redefinition of the Roles of Multi-level CNN Features
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.
Cross-Region Domain Adaptation for Class-level Alignment
To cope with this, we propose a method that applies adversarial training to align two feature distributions in the target domain.
Ranked #1 on
Domain Adaptation
on Synscapes-to-Cityscapes
Matching in the Dark: A Dataset for Matching Image Pairs of Low-light Scenes
To consider if and how well we can utilize such information stored in RAW-format images for image matching, we have created a new dataset named MID (matching in the dark).
Progressive and Selective Fusion Network for High Dynamic Range Imaging
We propose a novel method that can better fuse the features based on two ideas.
Look Wide and Interpret Twice: Improving Performance on Interactive Instruction-following Tasks
It then integrates the prediction with the visual information etc., yielding the final prediction of an action and an object.
Pushing the Envelope of Thin Crack Detection
We experimentally show that this makes it possible to detect cracks from an image of one-third the resolution of images used for annotation with about the same accuracy.
Bridging In- and Out-of-distribution Samples for Their Better Discriminability
We estimate where the generated samples by a single image transformation lie between ID and OOD using a network trained on clean ID samples.
Practical Evaluation of Out-of-Distribution Detection Methods for Image Classification
We reconsider the evaluation of OOD detection methods for image recognition.
Learning To Bundle-Adjust: A Graph Network Approach to Faster Optimization of Bundle Adjustment for Vehicular SLAM
Bundle adjustment (BA) occupies a large portion of SfM and visual SLAM's total execution time.
How Can CNNs Use Image Position for Segmentation?
We draw a mixed conclusion from the experimental results; the positional encoding certainly works in some cases, but the absolute image position may not be so important for segmentation tasks as we think.
Efficient Attention Mechanism for Visual Dialog that can Handle All the Interactions between Multiple Inputs
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.
Ranked #7 on
Visual Dialog
on Visual Dialog v1.0 test-std
Analysis of Deep Networks for Monocular Depth Estimation Through Adversarial Attacks with Proposal of a Defense Method
However, the prediction of saliency maps is itself vulnerable to the attacks, even though it is not the direct target of the attacks.
Analysis and a Solution of Momentarily Missed Detection for Anchor-based Object Detectors
The employment of convolutional neural networks has led to significant performance improvement on the task of object detection.
Restoring Images with Unknown Degradation Factors by Recurrent Use of a Multi-branch Network
The employment of convolutional neural networks has achieved unprecedented performance in the task of image restoration for a variety of degradation factors.
Evaluating Artificial Systems for Pairwise Ranking Tasks Sensitive to Individual Differences
There is another type of tasks for which what to predict is human perception itself, in which there are often individual differences.
Hyperparameter-Free Out-of-Distribution Detection Using Softmax of Scaled Cosine Similarity
The ability to detect out-of-distribution (OOD) samples is vital to secure the reliability of deep neural networks in real-world applications.
Improving Head Pose Estimation with a Combined Loss and Bounding Box Margin Adjustment
We address a problem of estimating pose of a person's head from its RGB image.
Visualization of Convolutional Neural Networks for Monocular Depth Estimation
We formulate it as an optimization problem of identifying the smallest number of image pixels from which the CNN can estimate a depth map with the minimum difference from the estimate from the entire image.
Dual Residual Networks Leveraging the Potential of Paired Operations for Image Restoration
In this paper, we study design of deep neural networks for tasks of image restoration.
Toward Explainable Fashion Recommendation
For this purpose, we propose a method for quantifying how influential each feature of each item is to the score.
Multi-task Learning of Hierarchical Vision-Language Representation
The representation is hierarchical, and prediction for each task is computed from the representation at its corresponding level of the hierarchy.
Attention-based Adaptive Selection of Operations for Image Restoration in the Presence of Unknown Combined Distortions
There are many different types of distortion which affect image quality.
Feature Quantization for Defending Against Distortion of Images
In this work, we address the problem of improving robustness of convolutional neural networks (CNNs) to image distortion.
Recommending Outfits from Personal Closet
We consider grading a fashion outfit for recommendation, where we assume that users have a closet of items and we aim at producing a score for an arbitrary combination of items in the closet.
Improved Fusion of Visual and Language Representations by Dense Symmetric Co-Attention for Visual Question Answering
A key solution to visual question answering (VQA) exists in how to fuse visual and language features extracted from an input image and question.
Revisiting Single Image Depth Estimation: Toward Higher Resolution Maps with Accurate Object Boundaries
Experimental results show that these two improvements enable to attain higher accuracy than the current state-of-the-arts, which is given by finer resolution reconstruction, for example, with small objects and object boundaries.
Ranked #61 on
Monocular Depth Estimation
on NYU-Depth V2
Exploiting the Potential of Standard Convolutional Autoencoders for Image Restoration by Evolutionary Search
Researchers have applied deep neural networks to image restoration tasks, in which they proposed various network architectures, loss functions, and training methods.
Deep Structured Energy-Based Image Inpainting
In this paper, we propose a structured image inpainting method employing an energy based model.
A vision based system for underwater docking
In the experiments, we first evaluate performance of the proposed detection module on UDID and its deformed variations.
HyperNetworks with statistical filtering for defending adversarial examples
This problem was addressed by employing several defense methods for detection and rejection of particular types of attacks.
End-to-end learning potentials for structured attribute prediction
We present a structured inference approach in deep neural networks for multiple attribute prediction.
Linear Discriminant Generative Adversarial Networks
We develop a novel method for training of GANs for unsupervised and class conditional generation of images, called Linear Discriminant GAN (LD-GAN).
Improving Robustness of Feature Representations to Image Deformations using Powered Convolution in CNNs
In this work, we address the problem of improvement of robustness of feature representations learned using convolutional neural networks (CNNs) to image deformation.
Information Potential Auto-Encoders
In order to estimate the entropy of the encoding variables and the mutual information, we propose a non-parametric method.
Truncating Wide Networks using Binary Tree Architectures
By employing the proposed architecture on a baseline wide network, we can construct and train a new network with same depth but considerably less number of parameters.
Optimization on Product Submanifolds of Convolution Kernels
The results show that geometric adaptive step size computation methods of G-SGD can improve training loss and convergence properties of CNNs.
Self-calibration-based Approach to Critical Motion Sequences of Rolling-shutter Structure from Motion
In this paper we consider critical motion sequences (CMSs) of rolling-shutter (RS) SfM.
Optimization on Submanifolds of Convolution Kernels in CNNs
Following our theoretical results, we propose a SGD algorithm with assurance of almost sure convergence of the methods to a solution at single minimum of classification loss of CNNs.
Automatic Attribute Discovery with Neural Activations
This paper proposes an automatic approach to discover and analyze visual attributes from a noisy collection of image-text data on the Web.
Design of Kernels in Convolutional Neural Networks for Image Classification
Despite the effectiveness of Convolutional Neural Networks (CNNs) for image classification, our understanding of the relationship between shape of convolution kernels and learned representations is limited.
Integrating Deep Features for Material Recognition
We propose a method for integration of features extracted using deep representations of Convolutional Neural Networks (CNNs) each of which is learned using a different image dataset of objects and materials for material recognition.
Transformation of Markov Random Fields for Marginal Distribution Estimation
Although downsizing MRFs should directly reduce the computational cost, there is no systematic way of doing this, since it is unclear how to obtain the MRF energy for the downsized MRFs and also how to translate the estimates of their marginal distributions to those of the original MRFs.
Detecting Changes in 3D Structure of a Scene from Multi-view Images Captured by a Vehicle-Mounted Camera
The proposed method is compared with the methods that use multi-view stereo (MVS) to reconstruct the scene structures of the two time points and then differentiate them to detect changes.
Discrete MRF Inference of Marginal Densities for Non-uniformly Discretized Variable Space
In this paper, we show a novel formulation for this continuous-discrete conversion.
