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20162022
most citedPCAMs: Weakly Supervised Semantic Segmentation Using Point Supervision

11 citations · 54 across the 19 of their papers we have counts for

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Showing 2020Show all

8 papers · 1 filter

cs.CV20201 cited

StressNet: Detecting Stress in Thermal Videos

Satish Kumar, A S M Iftekhar, Michael Goebel +7

Precise measurement of physiological signals is critical for the effective monitoring of human vital signs. Recent developments in computer vision have demonstrated that signals su…

cs.CV2020

Semi supervised segmentation and graph-based tracking of 3D nuclei in time-lapse microscopy

S. Shailja, Jiaxiang Jiang, B. S. Manjunath

We propose a novel weakly supervised method to improve the boundary of the 3D segmented nuclei utilizing an over-segmented image. This is motivated by the observation that current…

cs.CV2020

Exploiting Context for Robustness to Label Noise in Active Learning

Sudipta Paul, Shivkumar Chandrasekaran, B. S. Manjunath +1

Several works in computer vision have demonstrated the effectiveness of active learning for adapting the recognition model when new unlabeled data becomes available. Most of these…

eess.IV20204 cited

Adversarial Attacks on Co-Occurrence Features for GAN Detection

Michael Goebel, B. S. Manjunath

Improvements in Generative Adversarial Networks (GANs) have greatly reduced the difficulty of producing new, photo-realistic images with unique semantic meaning. With this rise in…

cs.CV20202 cited

3DMaterialGAN: Learning 3D Shape Representation from Latent Space for Materials Science Applications

Devendra K. Jangid, Neal R. Brodnik, Amil Khan +4

In the field of computer vision, unsupervised learning for 2D object generation has advanced rapidly in the past few years. However, 3D object generation has not garnered the same…

eess.IV202010 cited

Detection, Attribution and Localization of GAN Generated Images

Michael Goebel, Lakshmanan Nataraj, Tejaswi Nanjundaswamy +3

Recent advances in Generative Adversarial Networks (GANs) have led to the creation of realistic-looking digital images that pose a major challenge to their detection by humans or c…