4 code implementations • 2 May 2019 • I. Zeki Yalniz, Hervé Jégou, Kan Chen, Manohar Paluri, Dhruv Mahajan
This paper presents a study of semi-supervised learning with large convolutional networks.
Ranked #6 on Image Classification on OmniBenchmark (using extra training data)
1 code implementation • ECCV 2020 • Ali Diba, Mohsen Fayyaz, Vivek Sharma, Manohar Paluri, Jurgen Gall, Rainer Stiefelhagen, Luc van Gool
HVU is organized hierarchically in a semantic taxonomy that focuses on multi-label and multi-task video understanding as a comprehensive problem that encompasses the recognition of multiple semantic aspects in the dynamic scene.
Ranked #11 on Action Recognition on UCF101
no code implementations • 26 Dec 2018 • Mohamed Elhoseiny, Francesca Babiloni, Rahaf Aljundi, Marcus Rohrbach, Manohar Paluri, Tinne Tuytelaars
So far life-long learning (LLL) has been studied in relatively small-scale and relatively artificial setups.
no code implementations • ECCV 2018 • Jamie Ray, Heng Wang, Du Tran, YuFei Wang, Matt Feiszli, Lorenzo Torresani, Manohar Paluri
The videos retrieved by the search engines are then veried for correctness by human annotators.
no code implementations • CVPR 2018 • De-An Huang, Vignesh Ramanathan, Dhruv Mahajan, Lorenzo Torresani, Manohar Paluri, Li Fei-Fei, Juan Carlos Niebles
The ability to capture temporal information has been critical to the development of video understanding models.
4 code implementations • ECCV 2018 • Dhruv Mahajan, Ross Girshick, Vignesh Ramanathan, Kaiming He, Manohar Paluri, Yixuan Li, Ashwin Bharambe, Laurens van der Maaten
ImageNet classification is the de facto pretraining task for these models.
Ranked #222 on Image Classification on ImageNet (using extra training data)
2 code implementations • 27 Apr 2018 • Ji Zhang, Yannis Kalantidis, Marcus Rohrbach, Manohar Paluri, Ahmed Elgammal, Mohamed Elhoseiny
Large scale visual understanding is challenging, as it requires a model to handle the widely-spread and imbalanced distribution of <subject, relation, object> triples.
1 code implementation • CVPR 2018 • Rohit Girdhar, Georgia Gkioxari, Lorenzo Torresani, Manohar Paluri, Du Tran
This paper addresses the problem of estimating and tracking human body keypoints in complex, multi-person video.
Ranked #8 on Pose Tracking on PoseTrack2017 (using extra training data)
20 code implementations • CVPR 2018 • Du Tran, Heng Wang, Lorenzo Torresani, Jamie Ray, Yann Lecun, Manohar Paluri
In this paper we discuss several forms of spatiotemporal convolutions for video analysis and study their effects on action recognition.
Ranked #3 on Action Recognition on Sports-1M
1 code implementation • 16 Aug 2017 • Du Tran, Jamie Ray, Zheng Shou, Shih-Fu Chang, Manohar Paluri
Learning image representations with ConvNets by pre-training on ImageNet has proven useful across many visual understanding tasks including object detection, semantic segmentation, and image captioning.
Ranked #71 on Action Recognition on HMDB-51
no code implementations • CVPR 2017 • Vijay Kumar, Anoop Namboodiri, Manohar Paluri, C. V. Jawahar
Person recognition methods that use multiple body regions have shown significant improvements over traditional face-based recognition.
no code implementations • 20 May 2017 • Tim Danford, Onur Filiz, Jing Huang, Brian Karrer, Manohar Paluri, Guan Pang, Vish Ponnampalam, Nicolas Stier-Moses, Birce Tezel
This article discusses a framework to support the design and end-to-end planning of fixed millimeter-wave networks.
2 code implementations • 15 Jul 2016 • Song Han, Jeff Pool, Sharan Narang, Huizi Mao, Enhao Gong, Shijian Tang, Erich Elsen, Peter Vajda, Manohar Paluri, John Tran, Bryan Catanzaro, William J. Dally
We propose DSD, a dense-sparse-dense training flow, for regularizing deep neural networks and achieving better optimization performance.
no code implementations • 23 Jun 2016 • Du Tran, Maksim Bolonkin, Manohar Paluri, Lorenzo Torresani
Language has been exploited to sidestep the problem of defining video categories, by formulating video understanding as the task of captioning or description.
no code implementations • 20 Nov 2015 • Du Tran, Lubomir Bourdev, Rob Fergus, Lorenzo Torresani, Manohar Paluri
Over the last few years deep learning methods have emerged as one of the most prominent approaches for video analysis.
2 code implementations • 18 Nov 2015 • Oren Rippel, Manohar Paluri, Piotr Dollar, Lubomir Bourdev
Beyond classification, we further validate the saliency of the learnt representations via their attribute concentration and hierarchy recovery properties, achieving 10-25% relative gains on the softmax classifier and 25-50% on triplet loss in these tasks.
no code implementations • CVPR 2016 • Yin Li, Manohar Paluri, James M. Rehg, Piotr Dollár
In this work we present a simple yet effective approach for training edge detectors without human supervision.
no code implementations • CVPR 2016 • Chen Sun, Manohar Paluri, Ronan Collobert, Ram Nevatia, Lubomir Bourdev
This paper aims to classify and locate objects accurately and efficiently, without using bounding box annotations.
Ranked #5 on Weakly Supervised Object Detection on MS COCO
no code implementations • ICCV 2015 • Kevin Tang, Manohar Paluri, Li Fei-Fei, Rob Fergus, Lubomir Bourdev
With the widespread availability of cellphones and cameras that have GPS capabilities, it is common for images being uploaded to the Internet today to have GPS coordinates associated with them.
no code implementations • CVPR 2015 • Ning Zhang, Manohar Paluri, Yaniv Taigman, Rob Fergus, Lubomir Bourdev
We explore the task of recognizing peoples' identities in photo albums in an unconstrained setting.
28 code implementations • ICCV 2015 • Du Tran, Lubomir Bourdev, Rob Fergus, Lorenzo Torresani, Manohar Paluri
We propose a simple, yet effective approach for spatiotemporal feature learning using deep 3-dimensional convolutional networks (3D ConvNets) trained on a large scale supervised video dataset.
Ranked #8 on Action Recognition on Sports-1M
Action Recognition In Videos Dynamic Facial Expression Recognition
no code implementations • 9 Jun 2014 • Sainbayar Sukhbaatar, Joan Bruna, Manohar Paluri, Lubomir Bourdev, Rob Fergus
The availability of large labeled datasets has allowed Convolutional Network models to achieve impressive recognition results.
1 code implementation • CVPR 2014 • Ning Zhang, Manohar Paluri, Marc'Aurelio Ranzato, Trevor Darrell, Lubomir Bourdev
We propose a method for inferring human attributes (such as gender, hair style, clothes style, expression, action) from images of people under large variation of viewpoint, pose, appearance, articulation and occlusion.
Ranked #7 on Facial Attribute Classification on LFWA