1 code implementation • 20 Oct 2022 • Megha Nawhal, Akash Abdu Jyothi, Greg Mori
Action anticipation involves predicting future actions having observed the initial portion of a video.
no code implementations • ECCV 2020 • Mengyao Zhai, Lei Chen, JiaWei He, Megha Nawhal, Frederick Tung, Greg Mori
In contrast, we propose a parameter efficient framework, Piggyback GAN, which learns the current task by building a set of convolutional and deconvolutional filters that are factorized into filters of the models trained on previous tasks.
no code implementations • 21 Jan 2021 • Megha Nawhal, Greg Mori
Detecting and localizing action instances in untrimmed videos requires reasoning over multiple action instances in a video.
Ranked #3 on Temporal Action Localization on THUMOS’14 (mAP IOU@0.1 metric)
1 code implementation • 6 Jul 2020 • Xiang Xu, Megha Nawhal, Greg Mori, Manolis Savva
We present a mutual information-based framework for unsupervised image-to-image translation.
no code implementations • ECCV 2020 • Megha Nawhal, Mengyao Zhai, Andreas Lehrmann, Leonid Sigal, Greg Mori
Human activity videos involve rich, varied interactions between people and objects.
no code implementations • pproximateinference AABI Symposium 2019 • Yu Gong, Hossein Hajimirsadeghi, JiaWei He, Megha Nawhal, Thibaut Durand, Greg Mori
Despite promising progress on unimodal data imputation (e. g. image inpainting), models for multimodal data imputation are far from satisfactory.
no code implementations • 25 Sep 2019 • Yu Gong, Hossein Hajimirsadeghi, JiaWei He, Megha Nawhal, Thibaut Durand, Greg Mori
Learning from only partially-observed data for imputation has been an active research area.
no code implementations • 7 Aug 2019 • Zhiwei Deng, Megha Nawhal, Lili Meng, Greg Mori
In this paper, we propose Continuous Graph Flow, a generative continuous flow based method that aims to model complex distributions of graph-structured data.
no code implementations • ICCV 2019 • Mengyao Zhai, Lei Chen, Fred Tung, JiaWei He, Megha Nawhal, Greg Mori
This makes it possible to perform image-conditioned generation tasks in a lifelong learning setting.