1 code implementation • ECCV 2020 • Xuefeng Hu, Zhihan Zhang, Zhenye Jiang, Syomantak Chaudhuri, Zhenheng Yang, Ram Nevatia
Tehchniques for manipulating images are advancing rapidly; while these are helpful for many useful tasks, they also pose a threat to society with their ability to create believable misinformation.
Ranked #5 on Image Manipulation Localization on Columbia
no code implementations • CVPR 2021 • Qing Liu, Vignesh Ramanathan, Dhruv Mahajan, Alan Yuille, Zhenheng Yang
However, existing approaches which rely only on image-level class labels predominantly suffer from errors due to (a) partial segmentation of objects and (b) missing object predictions.
no code implementations • 1 Sep 2020 • Xuefeng Hu, Zhihan Zhang, Zhenye Jiang, Syomantak Chaudhuri, Zhenheng Yang, Ram Nevatia
We present a novel framework, Spatial Pyramid Attention Network (SPAN) for detection and localization of multiple types of image manipulations.
no code implementations • CVPR 2019 • Zhenheng Yang, Dhruv Mahajan, Deepti Ghadiyaram, Ram Nevatia, Vignesh Ramanathan
Weakly supervised object detection aims at reducing the amount of supervision required to train detection models.
Ranked #1 on Weakly Supervised Object Detection on Charades
1 code implementation • 14 Oct 2018 • Chenxu Luo, Zhenheng Yang, Peng Wang, Yang Wang, Wei Xu, Ram Nevatia, Alan Yuille
Performance on the five tasks of depth estimation, optical flow estimation, odometry, moving object segmentation and scene flow estimation shows that our approach outperforms other SoTA methods.
1 code implementation • 8 Oct 2018 • Yang Wang, Zhenheng Yang, Peng Wang, Yi Yang, Chenxu Luo, Wei Xu
Then the whole scene is decomposed into moving foreground and static background by compar- ing the estimated optical flow and rigid flow derived from the depth and ego-motion.
no code implementations • 27 Jun 2018 • Zhenheng Yang, Peng Wang, Yang Wang, Wei Xu, Ram Nevatia
The four types of information, i. e. 2D flow, camera pose, segment mask and depth maps, are integrated into a differentiable holistic 3D motion parser (HMP), where per-pixel 3D motion for rigid background and moving objects are recovered.
1 code implementation • CVPR 2018 • Zhenheng Yang, Peng Wang, Yang Wang, Wei Xu, Ram Nevatia
In our framework, the predicted depths, normals and edges are forced to be consistent all the time.
no code implementations • CVPR 2018 • Yang Wang, Yi Yang, Zhenheng Yang, Liang Zhao, Peng Wang, Wei Xu
Especially on KITTI dataset where abundant unlabeled samples exist, our unsupervised method outperforms its counterpart trained with supervised learning.
no code implementations • 10 Nov 2017 • Zhenheng Yang, Peng Wang, Wei Xu, Liang Zhao, Ramakant Nevatia
Learning to reconstruct depths in a single image by watching unlabeled videos via deep convolutional network (DCN) is attracting significant attention in recent years.
no code implementations • 31 Jul 2017 • Zhenheng Yang, Jiyang Gao, Ram Nevatia
In this work, we address the problem of spatio-temporal action detection in temporally untrimmed videos.
1 code implementation • 16 Jul 2017 • Jiyang Gao, Zhenheng Yang, Ram Nevatia
RED takes multiple history representations as input and learns to anticipate a sequence of future representations.
12 code implementations • ICCV 2017 • Jiyang Gao, Chen Sun, Zhenheng Yang, Ram Nevatia
For evaluation, we adopt TaCoS dataset, and build a new dataset for this task on top of Charades by adding sentence temporal annotations, called Charades-STA.
no code implementations • 2 May 2017 • Jiyang Gao, Zhenheng Yang, Ram Nevatia
CBR uses temporal coordinate regression to refine the temporal boundaries of the sliding windows.
Ranked #6 on Temporal Action Localization on THUMOS’14 (mAP IOU@0.1 metric)
1 code implementation • ICCV 2017 • Jiyang Gao, Zhenheng Yang, Chen Sun, Kan Chen, Ram Nevatia
Temporal Action Proposal (TAP) generation is an important problem, as fast and accurate extraction of semantically important (e. g. human actions) segments from untrimmed videos is an important step for large-scale video analysis.
Ranked #8 on Action Recognition on THUMOS’14
no code implementations • 12 Sep 2016 • Zhenheng Yang, Ram Nevatia
The number of proposals is decreased after each level, and the areas of regions are decreased to more precisely fit the face.