Search Results for author:
Found 13 papers, 2 papers with code
SACA Net: Cybersickness Assessment of Individual Viewers for VR Content via Graph-based Symptom Relation Embedding
In this paper, we propose a novel symptom-aware cybersickness assessment network (SACA Net) that quantifies physical symptom levels for assessing cybersickness of individual viewers.
MSCoTDet: Language-driven Multi-modal Fusion for Improved Multispectral Pedestrian Detection
Specifically, we generate text descriptions of the pedestrian in each RGB and thermal modality and design a Multispectral Chain-of-Thought (MSCoT) prompting, which models a step-by-step process to facilitate cross-modal reasoning at the semantic level and perform accurate detection.
Causal Mode Multiplexer: A Novel Framework for Unbiased Multispectral Pedestrian Detection
As a result, multispectral pedestrian detectors show poor generalization ability on examples beyond this statistical correlation, such as ROTX data.
Towards a Better Understanding of VR Sickness: Physical Symptom Prediction for VR Contents
To make better understanding of VR sickness, it is required to predict and provide the level of major symptoms of VR sickness rather than overall degree of VR sickness.
Visual Comfort Aware-Reinforcement Learning for Depth Adjustment of Stereoscopic 3D Images
For a human expert, the depth adjustment procedure is a sequence of iterative decision making.
Video Prediction Recalling Long-term Motion Context via Memory Alignment Learning
Our work addresses long-term motion context issues for predicting future frames.
Ranked #1 on
Video Prediction
on KTH
(Cond metric)
Generative Guiding Block: Synthesizing Realistic Looking Variants Capable of Even Large Change Demands
Generating realistic looking images with large variations (e. g., large spatial deformations and large pose change), however, is very challenging.
ICADx: Interpretable computer aided diagnosis of breast masses
In this paper, we investigate interpretability in CADx with the proposed interpretable CADx (ICADx) framework.
STAN: Spatio-Temporal Adversarial Networks for Abnormal Event Detection
In this paper, we propose a novel abnormal event detection method with spatio-temporal adversarial networks (STAN).
Ranked #17 on
Anomaly Detection
on ShanghaiTech
VR IQA NET: Deep Virtual Reality Image Quality Assessment using Adversarial Learning
The proposed human perception guider criticizes the predicted quality score of the predictor with the human perceptual score using adversarial learning.
Measurement of exceptional motion in VR video contents for VR sickness assessment using deep convolutional autoencoder
This paper proposes a new objective metric of exceptional motion in VR video contents for VR sickness assessment.
Iterative Deep Convolutional Encoder-Decoder Network for Medical Image Segmentation
In this paper, we propose a novel medical image segmentation using iterative deep learning framework.
Modality-bridge Transfer Learning for Medical Image Classification
This paper presents a new approach of transfer learning-based medical image classification to mitigate insufficient labeled data problem in medical domain.
