Search Results for author:
Found 8 papers, 4 papers with code
Unified Fully and Timestamp Supervised Temporal Action Segmentation via Sequence to Sequence Translation
This paper introduces a unified framework for video action segmentation via sequence to sequence (seq2seq) translation in a fully and timestamp supervised setup.
Ranked #4 on
Action Segmentation
on Assembly101
How to augment your ViTs? Consistency loss and StyleAug, a random style transfer augmentation
In this work, we empirical evaluated how different data augmentation strategies performed on CNN (e. g., ResNet) versus ViT architectures for image classification.
No-Reference Image Quality Assessment via Transformers, Relative Ranking, and Self-Consistency
Specifically, we enforce self-consistency between the outputs of our quality assessment model for each image and its transformation (horizontally flipped) to utilize the rich self-supervisory information and reduce the uncertainty of the model.
Ranked #3 on
No-Reference Image Quality Assessment
on TID2013
3D Human Motion Estimation via Motion Compression and Refinement
Experiments show that our method produces both smooth and accurate 3D human pose and motion estimates.
Ranked #14 on
3D Human Pose Estimation
on 3DPW
(Acceleration Error metric, using extra
training data)
Importance of Self-Consistency in Active Learning for Semantic Segmentation
We address the task of active learning in the context of semantic segmentation and show that self-consistency can be a powerful source of self-supervision to greatly improve the performance of a data-driven model with access to only a small amount of labeled data.
No-Reference Image Quality Assessment via Feature Fusion and Multi-Task Learning
In our experiments, we demonstrate that by utilizing multi-task learning and our proposed feature fusion method, our model yields better performance for the NR-IQA task.
Multi-Task Learning
No-Reference Image Quality Assessment
+1
Synthesized Texture Quality Assessment via Multi-scale Spatial and Statistical Texture Attributes of Image and Gradient Magnitude Coefficients
Performance evaluations on two synthesized texture databases demonstrate that our proposed RR synthesized texture quality metric significantly outperforms both full-reference and RR state-of-the-art quality metrics in predicting the perceived visual quality of the synthesized textures.
Spatially-Varying Blur Detection Based on Multiscale Fused and Sorted Transform Coefficients of Gradient Magnitudes
The detection of spatially-varying blur without having any information about the blur type is a challenging task.
