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Found 7 papers, 1 papers with code
Find it if You Can: End-to-End Adversarial Erasing for Weakly-Supervised Semantic Segmentation
Semantic segmentation is a task that traditionally requires a large dataset of pixel-level ground truth labels, which is time-consuming and expensive to obtain.
Ranked #25 on
Semantic Segmentation
on PASCAL VOC 2012 val
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
VATLD: A Visual Analytics System to Assess, Understand and Improve Traffic Light Detection
In this work, we propose a visual analytics system, VATLD, equipped with a disentangled representation learning and semantic adversarial learning, to assess, understand, and improve the accuracy and robustness of traffic light detectors in autonomous driving applications.
I Bet You Are Wrong: Gambling Adversarial Networks for Structured Semantic Segmentation
Adversarial training has been recently employed for realizing structured semantic segmentation, in which the aim is to preserve higher-level scene structural consistencies in dense predictions.
Dynamic Adaptation on Non-Stationary Visual Domains
To demonstrate the effectiveness of our proposed framework, we modify associative domain adaptation to work well on source and target data batches with unequal class distributions.
EL-GAN: Embedding Loss Driven Generative Adversarial Networks for Lane Detection
Convolutional neural networks have been successfully applied to semantic segmentation problems.
Ranked #15 on
Lane Detection
on TuSimple
US-Cut: Interactive Algorithm for rapid Detection and Segmentation of Liver Tumors in Ultrasound Acquisitions
We present an interactive segmentation approach for liver tumors in US acquisitions.
Interactive Volumetry Of Liver Ablation Zones
The visual feedback and interactivity make the proposed tool well suitable for the clinical workflow.
