activity
20112023
most citedTraining Deeper Convolutional Networks with Deep Supervision

167 citations · 546 across the 27 of their papers we have counts for

collaborators

34 papers

cs.CV2023

JoIN: Joint GANs Inversion for Intrinsic Image Decomposition

Viraj Shah, Svetlana Lazebnik, Julien Philip

Intrinsic Image Decomposition (IID) is a challenging inverse problem that seeks to decompose a natural image into its underlying intrinsic components such as albedo and shading. Wh…

cs.CV2023

One-Shot Stylization for Full-Body Human Images

Aiyu Cui, Svetlana Lazebnik

The goal of human stylization is to transfer full-body human photos to a style specified by a single art character reference image. Although previous work has succeeded in example-…

cs.CV2022★ 1 cited

Robust Online Video Instance Segmentation with Track Queries

Zitong Zhan, Daniel McKee, Svetlana Lazebnik

Recently, transformer-based methods have achieved impressive results on Video Instance Segmentation (VIS). However, most of these top-performing methods run in an offline manner by…

cs.CV2022

MultiStyleGAN: Multiple One-shot Image Stylizations using a Single GAN

Viraj Shah, Ayush Sarkar, Sudharsan Krishnakumar Anitha +1

Image stylization aims at applying a reference style to arbitrary input images. A common scenario is one-shot stylization, where only one example is available for each reference st…

cs.CV2022★ 2 cited

Transfer of Representations to Video Label Propagation: Implementation Factors Matter

Daniel McKee, Zitong Zhan, Bing Shuai +3

This work studies feature representations for dense label propagation in video, with a focus on recently proposed methods that learn video correspondence using self-supervised sign…

cs.CV2021

Interpretation of Emergent Communication in Heterogeneous Collaborative Embodied Agents

Shivansh Patel, Saim Wani, Unnat Jain +4

Communication between embodied AI agents has received increasing attention in recent years. Despite its use, it is still unclear whether the learned communication is interpretable…