SOTA2 Research · papers

Papers for “semantic segmentation”

Find papers, implementations, and the benchmark evidence behind state-of-the-art AI systems.

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20 indexed

Title	Authors	Year	Source
Self-Supervision with Superpixels: Training Few-shot Medical Image Segmentation without Annotation	Cheng Ouyang, Carlo Biffi, Chen Chen	2020	arxiv 2007.09886
FibeR-CNN: Expanding Mask R-CNN to Improve Image-Based Fiber Analysis	Max Frei, Frank Einar Kruis	2020	arxiv 2006.04552
Weakly Supervised Instance Segmentation by Deep Community Learning	Jaedong Hwang, Seohyun Kim, Jeany Son	2020	arxiv 2001.11207
Visual Transformers: Token-based Image Representation and Processing for Computer Vision	Bichen Wu, Chenfeng Xu, Xiaoliang Dai	2020	arxiv 2006.03677
Single-shot Path Integrated Panoptic Segmentation	Sukjun Hwang, Seoung Wug Oh, Seon Joo Kim	2020	arxiv 2012.01632
Self-Supervised Learning of Lidar Segmentation for Autonomous Indoor Navigation	Hugues Thomas, Ben Agro, Mona Gridseth	2020	arxiv 2012.05897
Classifying Breast Histopathology Images with a Ductal Instance-Oriented Pipeline	Beibin Li, Ezgi Mercan, Sachin Mehta	2020	arxiv 2012.06136
Deep Visual Domain Adaptation	Gabriela Csurka	2020	arxiv 2012.14176
Segmentation of skin lesions and their attributes using Generative Adversarial Networks	Cristian Lazo	2021	arxiv 2102.00169
Panoptic Lintention Network: Towards Efficient Navigational Perception for the Visually Impaired	Wei Mao, Jiaming Zhang, Kailun Yang	2021	arxiv 2103.04128
Uncertainty-guided Model Generalization to Unseen Domains