Enabling resource-efficient aiot system with cross-level optimization: A survey

S Liu, B Guo, C Fang, Z Wang, S Luo… - … Surveys & Tutorials, 2023 - ieeexplore.ieee.org
The emerging field of artificial intelligence of things (AIoT, AI+ IoT) is driven by the
widespread use of intelligent infrastructures and the impressive success of deep learning …

Vision-based autonomous vehicle systems based on deep learning: A systematic literature review

MI Pavel, SY Tan, A Abdullah - Applied Sciences, 2022 - mdpi.com
In the past decade, autonomous vehicle systems (AVS) have advanced at an exponential
rate, particularly due to improvements in artificial intelligence, which have had a significant …

Semi-supervised semantic segmentation using unreliable pseudo-labels

Y Wang, H Wang, Y Shen, J Fei, W Li… - Proceedings of the …, 2022 - openaccess.thecvf.com
The crux of semi-supervised semantic segmentation is to assign pseudo-labels to the pixels
of unlabeled images. A common practice is to select the highly confident predictions as the …

Rethinking semantic segmentation: A prototype view

T Zhou, W Wang, E Konukoglu… - Proceedings of the …, 2022 - openaccess.thecvf.com
Prevalent semantic segmentation solutions, despite their different network designs (FCN
based or attention based) and mask decoding strategies (parametric softmax based or pixel …

Cross-image relational knowledge distillation for semantic segmentation

C Yang, H Zhou, Z An, X Jiang… - Proceedings of the …, 2022 - openaccess.thecvf.com
Abstract Current Knowledge Distillation (KD) methods for semantic segmentation often
guide the student to mimic the teacher's structured information generated from individual …

Deep hierarchical semantic segmentation

L Li, T Zhou, W Wang, J Li… - Proceedings of the IEEE …, 2022 - openaccess.thecvf.com
Humans are able to recognize structured relations in observation, allowing us to decompose
complex scenes into simpler parts and abstract the visual world in multiple levels. However …

Regional semantic contrast and aggregation for weakly supervised semantic segmentation

T Zhou, M Zhang, F Zhao, J Li - Proceedings of the IEEE …, 2022 - openaccess.thecvf.com
Learning semantic segmentation from weakly-labeled (eg, image tags only) data is
challenging since it is hard to infer dense object regions from sparse semantic tags. Despite …

Gmmseg: Gaussian mixture based generative semantic segmentation models

C Liang, W Wang, J Miao… - Advances in Neural …, 2022 - proceedings.neurips.cc
Prevalent semantic segmentation solutions are, in essence, a dense discriminative classifier
of p (class| pixel feature). Though straightforward, this de facto paradigm neglects the …

Contrastive boundary learning for point cloud segmentation

L Tang, Y Zhan, Z Chen, B Yu… - Proceedings of the IEEE …, 2022 - openaccess.thecvf.com
Point cloud segmentation is fundamental in understanding 3D environments. However,
current 3D point cloud segmentation methods usually perform poorly on scene boundaries …

Sepico: Semantic-guided pixel contrast for domain adaptive semantic segmentation

B Xie, S Li, M Li, CH Liu, G Huang… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Domain adaptive semantic segmentation attempts to make satisfactory dense predictions on
an unlabeled target domain by utilizing the supervised model trained on a labeled source …