Search Results for author: Lior Shapira

Found 6 papers, 1 papers with code

MaskINT: Video Editing via Interpolative Non-autoregressive Masked Transformers

no code implementations19 Dec 2023 Haoyu Ma, Shahin Mahdizadehaghdam, Bichen Wu, Zhipeng Fan, YuChao Gu, Wenliang Zhao, Lior Shapira, Xiaohui Xie

Recent advances in generative AI have significantly enhanced image and video editing, particularly in the context of text prompt control.

Video Editing

2.5D Visual Relationship Detection

1 code implementation26 Apr 2021 Yu-Chuan Su, Soravit Changpinyo, Xiangning Chen, Sathish Thoppay, Cho-Jui Hsieh, Lior Shapira, Radu Soricut, Hartwig Adam, Matthew Brown, Ming-Hsuan Yang, Boqing Gong

To enable progress on this task, we create a new dataset consisting of 220k human-annotated 2. 5D relationships among 512K objects from 11K images.

Benchmarking Depth Estimation +2

Camera View Adjustment Prediction for Improving Image Composition

no code implementations15 Apr 2021 Yu-Chuan Su, Raviteja Vemulapalli, Ben Weiss, Chun-Te Chu, Philip Andrew Mansfield, Lior Shapira, Colvin Pitts

To address this issue, we propose a deep learning-based approach that provides suggestions to the photographer on how to adjust the camera view before capturing.

Image Cropping

Contrastive Learning for Label Efficient Semantic Segmentation

no code implementations ICCV 2021 Xiangyun Zhao, Raviteja Vemulapalli, Philip Andrew Mansfield, Boqing Gong, Bradley Green, Lior Shapira, Ying Wu

While recent Convolutional Neural Network (CNN) based semantic segmentation approaches have achieved impressive results by using large amounts of labeled training data, their performance drops significantly as the amount of labeled data decreases.

Contrastive Learning Segmentation +1

Contrastive Learning for Label-Efficient Semantic Segmentation

no code implementations13 Dec 2020 Xiangyun Zhao, Raviteja Vemulapalli, Philip Mansfield, Boqing Gong, Bradley Green, Lior Shapira, Ying Wu

While recent Convolutional Neural Network (CNN) based semantic segmentation approaches have achieved impressive results by using large amounts of labeled training data, their performance drops significantly as the amount of labeled data decreases.

Contrastive Learning Segmentation +1

ASIST: Automatic Semantically Invariant Scene Transformation

no code implementations4 Dec 2015 Or Litany, Tal Remez, Daniel Freedman, Lior Shapira, Alex Bronstein, Ran Gal

We present ASIST, a technique for transforming point clouds by replacing objects with their semantically equivalent counterparts.

Object

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