Search Results for author:
Found 34 papers, 15 papers with code
SMURF: Continuous Dynamics for Motion-Deblurring Radiance Fields
Neural radiance fields (NeRF) has attracted considerable attention for their exceptional ability in synthesizing novel views with high fidelity.
Synchronizing Vision and Language: Bidirectional Token-Masking AutoEncoder for Referring Image Segmentation
While these methods match language features with image features to effectively identify likely target objects, they often struggle to correctly understand contextual information in complex and ambiguous sentences and scenes.
Treating Motion as Option with Output Selection for Unsupervised Video Object Segmentation
Unsupervised video object segmentation (VOS) is a task that aims to detect the most salient object in a video without external guidance about the object.
On the Amplification of Linguistic Bias through Unintentional Self-reinforcement Learning by Generative Language Models -- A Perspective
Generative Language Models (GLMs) have the potential to significantly shape our linguistic landscape due to their expansive use in various digital applications.
ZIGNeRF: Zero-shot 3D Scene Representation with Invertible Generative Neural Radiance Fields
Generative Neural Radiance Fields (NeRFs) have demonstrated remarkable proficiency in synthesizing multi-view images by learning the distribution of a set of unposed images.
DuDGAN: Improving Class-Conditional GANs via Dual-Diffusion
We evaluated our method using the AFHQ, Food-101, and CIFAR-10 datasets and observed superior results across metrics such as FID, KID, Precision, and Recall score compared with comparison models, highlighting the effectiveness of our approach.
Game-Theoretical Analysis of Reviewer Rewards in Peer-Review Journal Systems: Analysis and Experimental Evaluation using Deep Reinforcement Learning
In this paper, we navigate the intricate domain of reviewer rewards in open-access academic publishing, leveraging the precision of mathematics and the strategic acumen of game theory.
GELU Activation Function in Deep Learning: A Comprehensive Mathematical Analysis and Performance
Selecting the most suitable activation function is a critical factor in the effectiveness of deep learning models, as it influences their learning capacity, stability, and computational efficiency.
Portfolio Optimization using Predictive Auxiliary Classifier Generative Adversarial Networks with Measuring Uncertainty
To obtain the expected returns, deep learning models have been explored in recent years.
Adaptive Graph Convolution Module for Salient Object Detection
Salient object detection (SOD) is a task that involves identifying and segmenting the most visually prominent object in an image.
Guided Slot Attention for Unsupervised Video Object Segmentation
Unsupervised video object segmentation aims to segment the most prominent object in a video sequence.
Two-stream Decoder Feature Normality Estimating Network for Industrial Anomaly Detection
Image reconstruction-based anomaly detection has recently been in the spotlight because of the difficulty of constructing anomaly datasets.
Class-Continuous Conditional Generative Neural Radiance Field
Additionally, we provide FIDs of generated 3D-aware images of each class of the datasets as it is possible to synthesize class-conditional images with $\text{C}^{3}$G-NeRF.
Ranked #2 on
Image Generation
on CelebA 128x128
Leveraging Spatio-Temporal Dependency for Skeleton-Based Action Recognition
In this paper, we propose the Spatio-Temporal Curve Network (STC-Net) to effectively leverage the spatio-temporal dependency of the human skeleton.
DP-NeRF: Deblurred Neural Radiance Field with Physical Scene Priors
The few studies that have investigated NeRF for blurred images have not considered geometric and appearance consistency in 3D space, which is one of the most important factors in 3D reconstruction.
Boundary-aware Camouflaged Object Detection via Deformable Point Sampling
The proposed DPS-Net utilizes a Deformable Point Sampling transformer (DPS transformer) that can effectively capture sparse local boundary information of significant object boundaries in COD using a deformable point sampling method.
Dual Prototype Attention for Unsupervised Video Object Segmentation
Unsupervised video object segmentation (VOS) aims to detect and segment the most salient object in videos.
Ranked #1 on
Unsupervised Video Object Segmentation
on FBMS test
Unsupervised Video Object Segmentation via Prototype Memory Network
The proposed model effectively extracts the RGB and motion information by extracting superpixel-based component prototypes from the input RGB images and optical flow maps.
Ranked #5 on
Unsupervised Video Object Segmentation
on FBMS test
Pixel-Level Equalized Matching for Video Object Segmentation
Feature similarity matching, which transfers the information of the reference frame to the query frame, is a key component in semi-supervised video object segmentation.
Treating Motion as Option to Reduce Motion Dependency in Unsupervised Video Object Segmentation
Unsupervised video object segmentation (VOS) aims to detect the most salient object in a video sequence at the pixel level.
Hierarchically Decomposed Graph Convolutional Networks for Skeleton-Based Action Recognition
Graph convolutional networks (GCNs) are the most commonly used methods for skeleton-based action recognition and have achieved remarkable performance.
Ranked #4 on
Skeleton Based Action Recognition
on NTU RGB+D 120
SPSN: Superpixel Prototype Sampling Network for RGB-D Salient Object Detection
However, despite advances in deep learning-based methods, RGB-D SOD is still challenging due to the large domain gap between an RGB image and the depth map and low-quality depth maps.
Ranked #3 on
RGB-D Salient Object Detection
on NJU2K
Tackling Background Distraction in Video Object Segmentation
Semi-supervised video object segmentation (VOS) aims to densely track certain designated objects in videos.
RandomSEMO: Normality Learning Of Moving Objects For Video Anomaly Detection
Moreover, MOLoss urges the model to focus on learning normal objects captured within RandomSEMO by amplifying the loss on the pixels near the moving objects.
Saliency Detection via Global Context Enhanced Feature Fusion and Edge Weighted Loss
1) Indiscriminately integrating the encoder feature, which contains spatial information for multiple objects, and the decoder feature, which contains global information of the salient object, is likely to convey unnecessary details of non-salient objects to the decoder, hindering saliency detection.
Ranked #1 on
RGB Salient Object Detection
on PASCAL-S
FastAno: Fast Anomaly Detection via Spatio-temporal Patch Transformation
Video anomaly detection has gained significant attention due to the increasing requirements of automatic monitoring for surveillance videos.
Anomaly Detection In Surveillance Videos
Optical Flow Estimation
+1
EdgeConv with Attention Module for Monocular Depth Estimation
Monocular depth estimation is an especially important task in robotics and autonomous driving, where 3D structural information is essential.
Robust Lane Detection via Expanded Self Attention
Modern deep learning methods achieve high performance in lane detection, but it is still difficult to accurately detect lanes in challenging situations such as congested roads and extreme lighting conditions.
Ranked #42 on
Lane Detection
on CULane
Regularization Strategy for Point Cloud via Rigidly Mixed Sample
Data augmentation is an effective regularization strategy to alleviate the overfitting, which is an inherent drawback of the deep neural networks.
Ranked #3 on
3D Point Cloud Classification
on ModelNet40-C
Estimation with Uncertainty via Conditional Generative Adversarial Networks
Such a deterministic nature in ANNs causes the limitations of using ANNs for medical diagnosis, law problems, and portfolio management, in which discovering not only the prediction but also the uncertainty of the prediction is essentially required.
Regularization Methods for Generative Adversarial Networks: An Overview of Recent Studies
However, applying GAN to different data types with diverse neural network architectures has been hindered by its limitation in training, where the model easily diverges.
Score-Guided Generative Adversarial Networks
We propose a Generative Adversarial Network (GAN) that introduces an evaluator module using pre-trained networks.
Controllable Generative Adversarial Network
The essential task of GAN is to control the features of samples generated from a random distribution.
