1k citations · 1.4k across the 12 of their papers we have counts for
9 papers · 1 filter
Deep End2End Voxel2Voxel Prediction
Du Tran, Lubomir Bourdev, Rob Fergus +2
Over the last few years deep learning methods have emerged as one of the most prominent approaches for video analysis. However, so far their most successful applications have been…
ProNet: Learning to Propose Object-specific Boxes for Cascaded Neural Networks
Chen Sun, Manohar Paluri, Ronan Collobert +2
This paper aims to classify and locate objects accurately and efficiently, without using bounding box annotations. It is challenging as objects in the wild could appear at arbitrar…
Improving Image Classification with Location Context
Kevin Tang, Manohar Paluri, Li Fei-Fei +2
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 associat…
Beyond Frontal Faces: Improving Person Recognition Using Multiple Cues
Ning Zhang, Manohar Paluri, Yaniv Taigman +2
We explore the task of recognizing peoples' identities in photo albums in an unconstrained setting. To facilitate this, we introduce the new People In Photo Albums (PIPA) dataset,…
Compressing Deep Convolutional Networks using Vector Quantization
Yunchao Gong, Liu Liu, Ming Yang +1
Deep convolutional neural networks (CNN) has become the most promising method for object recognition, repeatedly demonstrating record breaking results for image classification and…
Learning Spatiotemporal Features with 3D Convolutional Networks
Du Tran, Lubomir Bourdev, Rob Fergus +2
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 vi…