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20212024
most citedLatents2Segments: Disentangling the Latent Space of Generative Models for Semantic Segmentation of Face Images

1 citations · 1 across the 12 of their papers we have counts for

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12 papers

cs.CV2024

PNeRV: A Polynomial Neural Representation for Videos

Sonam Gupta, Snehal Singh Tomar, Grigorios G Chrysos +2

Extracting Implicit Neural Representations (INRs) on video data poses unique challenges due to the additional temporal dimension. In the context of videos, INRs have predominantly…

cs.CV2024

Spatially-Attentive Patch-Hierarchical Network with Adaptive Sampling for Motion Deblurring

Maitreya Suin, Kuldeep Purohit, A. N. Rajagopalan

This paper tackles the problem of motion deblurring of dynamic scenes. Although end-to-end fully convolutional designs have recently advanced the state-of-the-art in non-uniform mo…

cs.CV2023

Zero shot framework for satellite image restoration

Praveen Kandula, A. N. Rajagopalan

Satellite images are typically subject to multiple distortions. Different factors affect the quality of satellite images, including changes in atmosphere, surface reflectance, sun…

cs.CV2023

Unsupervised haze removal from underwater images

Praveen Kandula, A. N. Rajagopalan

Several supervised networks exist that remove haze information from underwater images using paired datasets and pixel-wise loss functions. However, training these networks requires…

cs.CV2023

Unsupervised network for low-light enhancement

Praveen Kandula, Maitreya Suin, A. N. Rajagopalan

Supervised networks address the task of low-light enhancement using paired images. However, collecting a wide variety of low-light/clean paired images is tedious as the scene needs…

cs.CV20221 cited

Latents2Segments: Disentangling the Latent Space of Generative Models for Semantic Segmentation of Face Images

Snehal Singh Tomar, A. N. Rajagopalan

With the advent of an increasing number of Augmented and Virtual Reality applications that aim to perform meaningful and controlled style edits on images of human faces, the impetu…