activity
20152026
most citedPosition, Padding and Predictions: A Deeper Look at Position Information in CNNs

42 citations · 163 across the 36 of their papers we have counts for

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Showing 2023Show all

7 papers · 1 filter

cs.CV2023

Reconstructive Latent-Space Neural Radiance Fields for Efficient 3D Scene Representations

Tristan Aumentado-Armstrong, Ashkan Mirzaei, Marcus A. Brubaker +4

Neural Radiance Fields (NeRFs) have proven to be powerful 3D representations, capable of high quality novel view synthesis of complex scenes. While NeRFs have been applied to graph…

cs.CV2023

GePSAn: Generative Procedure Step Anticipation in Cooking Videos

Mohamed Ashraf Abdelsalam, Samrudhdhi B. Rangrej, Isma Hadji +3

We study the problem of future step anticipation in procedural videos. Given a video of an ongoing procedural activity, we predict a plausible next procedure step described in rich…

cs.CV20231 cited

Dual-Camera Joint Deblurring-Denoising

Shayan Shekarforoush, Amanpreet Walia, Marcus A. Brubaker +2

Recent image enhancement methods have shown the advantages of using a pair of long and short-exposure images for low-light photography. These image modalities offer complementary s…

cs.CV20232 cited

Watch Your Steps: Local Image and Scene Editing by Text Instructions

Ashkan Mirzaei, Tristan Aumentado-Armstrong, Marcus A. Brubaker +4

Denoising diffusion models have enabled high-quality image generation and editing. We present a method to localize the desired edit region implicit in a text instruction. We levera…

cs.CV20232 cited

StepFormer: Self-supervised Step Discovery and Localization in Instructional Videos

Nikita Dvornik, Isma Hadji, Ran Zhang +4

Instructional videos are an important resource to learn procedural tasks from human demonstrations. However, the instruction steps in such videos are typically short and sparse, wi…

cs.CV2023

Reference-guided Controllable Inpainting of Neural Radiance Fields

Ashkan Mirzaei, Tristan Aumentado-Armstrong, Marcus A. Brubaker +4

The popularity of Neural Radiance Fields (NeRFs) for view synthesis has led to a desire for NeRF editing tools. Here, we focus on inpainting regions in a view-consistent and contro…