Semi-Supervised Authentically Distorted Image Quality Assessment With Consistency-Preserving Dual-Branch Convolutional Neural Network

Yue, Guanghui; Cheng, Daizhan; Li, Leida; Zhou, Tianwei; Liu, Hantao; Wang, Tianfu

doi:10.1109/tmm.2022.3209889

Cited by 18 publications

(2 citation statements)

References 63 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We remark that SSL has not been explored much even in the IQA literature. While Conde et al (Conde, Burchi, and Timofte 2022) study SSL methods for full reference IQA, some other methods (Wang, Li, and Ma 2021;Yue et al 2022) train an NR IQA model with a large number of labelled images and generate pseudo-labels on the unlabelled data.…”

Section: Related Workmentioning

confidence: 99%

Knowledge Guided Semi-supervised Learning for Quality Assessment of User Generated Videos

Mitra,

Soundararajan

2024

AAAI

View full text Add to dashboard Cite

Perceptual quality assessment of user generated content (UGC) videos is challenging due to the requirement of large scale human annotated videos for training. In this work, we address this challenge by first designing a self-supervised Spatio-Temporal Visual Quality Representation Learning (ST-VQRL) framework to generate robust quality aware features for videos. Then, we propose a dual-model based Semi Supervised Learning (SSL) method specifically designed for the Video Quality Assessment (SSL-VQA) task, through a novel knowledge transfer of quality predictions between the two models. Our SSL-VQA method uses the ST-VQRL backbone to produce robust performances across various VQA datasets including cross-database settings, despite being learned with limited human annotated videos. Our model improves the state-of-the-art performance when trained only with limited data by around 10%, and by around 15% when unlabelled data is also used in SSL. Source codes and checkpoints are available at https://github.com/Shankhanil006/SSL-VQA.

show abstract

Section: Related Workmentioning

confidence: 99%

Knowledge Guided Semi-supervised Learning for Quality Assessment of User Generated Videos

Mitra,

Soundararajan

2024

AAAI

View full text Add to dashboard Cite

show abstract

“…Recently, deep neural networks have been widely used in various fields, such as 3D shape clustering [32], quality assessment [33], and video enhancement [34]. Taking advantage of deep neural networks, researchers have conducted preliminary explorations of different visual tasks in subterranean scenes, which mainly contain the two categories of simultaneous localization and mapping (SLAM) [35][36][37] and object detection [38][39][40].…”

Section: Visual Tasks In Subterranean Scenesmentioning

confidence: 99%

Domain Adaptive Subterranean 3D Pedestrian Detection via Instance Transfer and Confidence Guidance

Liu,

Zheng,

Qin

et al. 2024

Electronics

View full text Add to dashboard Cite

With the exploration of subterranean scenes, determining how to ensure the safety of subterranean pedestrians has gradually become a hot research topic. Considering the poor illumination and lack of annotated data in subterranean scenes, it is essential to explore the LiDAR-based domain adaptive detectors for localizing the spatial location of pedestrians, thus providing instruction for evacuation and rescue. In this paper, a novel domain adaptive subterranean 3D pedestrian detection method is proposed to adapt pre-trained detectors from the annotated road scenes to the unannotated subterranean scenes. Specifically, an instance transfer-based scene updating strategy is designed to update the subterranean scenes by transferring instances from the road scenes to the subterranean scenes, aiming to create sufficient high-quality pseudo labels for fine-tuning the pre-trained detector. In addition, a pseudo label confidence-guided learning mechanism is constructed to fully utilize pseudo labels of different qualities under the guidance of confidence scores. Extensive experiments validate the superiority of our proposed domain adaptive subterranean 3D pedestrian detection method.

show abstract

meTMQI: multi-task and exposure-prior learning for Tone-Mapped Quality Index

Jiang,

Shen,

et al. 2024

Vis Comput

View full text Add to dashboard Cite

Semi-Supervised Authentically Distorted Image Quality Assessment With Consistency-Preserving Dual-Branch Convolutional Neural Network

Abstract: Cit a tio n fo r fin al p u blis h e d ve r sio n:Yue, G u a n g h ui, C h e n g, Di, Li, Lei d a, Z h o u, Tia n w ei, Liu, H a n t a o ORCID: h t t p s://o r ci d.

Cited by 18 publications

References 63 publications

Knowledge Guided Semi-supervised Learning for Quality Assessment of User Generated Videos

Knowledge Guided Semi-supervised Learning for Quality Assessment of User Generated Videos

Domain Adaptive Subterranean 3D Pedestrian Detection via Instance Transfer and Confidence Guidance

meTMQI: multi-task and exposure-prior learning for Tone-Mapped Quality Index

Contact Info

Product

Resources

About