Search Results for author: Lubomir Bourdev

Found 18 papers, 5 papers with code

PIM: Video Coding using Perceptual Importance Maps

no code implementations20 Dec 2022 Evgenya Pergament, Pulkit Tandon, Oren Rippel, Lubomir Bourdev, Alexander G. Anderson, Bruno Olshausen, Tsachy Weissman, Sachin Katti, Kedar Tatwawadi

The contributions of this work are threefold: (1) we introduce a web-tool which allows scalable collection of fine-grained perceptual importance, by having users interactively paint spatio-temporal maps over encoded videos; (2) we use this tool to collect a dataset with 178 videos with a total of 14443 frames of human annotated spatio-temporal importance maps over the videos; and (3) we use our curated dataset to train a lightweight machine learning model which can predict these spatio-temporal importance regions.

Video Compression

An Interactive Annotation Tool for Perceptual Video Compression

1 code implementation8 May 2022 Evgenya Pergament, Pulkit Tandon, Kedar Tatwawadi, Oren Rippel, Lubomir Bourdev, Bruno Olshausen, Tsachy Weissman, Sachin Katti, Alexander G. Anderson

We use this tool to collect data in-the-wild (10 videos, 17 users) and utilize the obtained importance maps in the context of x264 coding to demonstrate that the tool can indeed be used to generate videos which, at the same bitrate, look perceptually better through a subjective study - and are 1. 9 times more likely to be preferred by viewers.

Video Compression

ELF-VC: Efficient Learned Flexible-Rate Video Coding

no code implementations ICCV 2021 Oren Rippel, Alexander G. Anderson, Kedar Tatwawadi, Sanjay Nair, Craig Lytle, Lubomir Bourdev

In this setting, for natural videos our approach compares favorably across the entire R-D curve under metrics PSNR, MS-SSIM and VMAF against all mainstream video standards (H. 264, H. 265, AV1) and all ML codecs.


Real-Time Adaptive Image Compression

no code implementations ICML 2017 Oren Rippel, Lubomir Bourdev

We present a machine learning-based approach to lossy image compression which outperforms all existing codecs, while running in real-time.

Image Compression

Deep End2End Voxel2Voxel Prediction

no code implementations20 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.

Neural Architecture Search Optical Flow Estimation +3

Metric Learning with Adaptive Density Discrimination

2 code implementations18 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.

Classification Fine-Grained Visual Recognition +2

Web Scale Photo Hash Clustering on A Single Machine

no code implementations CVPR 2015 Yunchao Gong, Marcin Pawlowski, Fei Yang, Louis Brandy, Lubomir Bourdev, Rob Fergus

In addition, we propose an online clustering method based on binary k-means that is capable of clustering large photo stream on a single machine, and show applications to spam detection and trending photo discovery.

Clustering Online Clustering +1

Improving Image Classification with Location Context

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.

Classification General Classification +2

Compressing Deep Convolutional Networks using Vector Quantization

no code implementations18 Dec 2014 Yunchao Gong, Liu Liu, Ming Yang, Lubomir Bourdev

In this paper, we tackle this model storage issue by investigating information theoretical vector quantization methods for compressing the parameters of CNNs.

Classification Clustering +6

Learning Spatiotemporal Features with 3D Convolutional Networks

24 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.

Action Recognition In Videos Dynamic Facial Expression Recognition

Deep Poselets for Human Detection

no code implementations2 Jul 2014 Lubomir Bourdev, Fei Yang, Rob Fergus

We train the poselet model on top of PDF features and combine them with object-level CNNs for detection and bounding box prediction.

Human Detection

Training Convolutional Networks with Noisy Labels

no code implementations9 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.

General Classification

Microsoft COCO: Common Objects in Context

33 code implementations1 May 2014 Tsung-Yi Lin, Michael Maire, Serge Belongie, Lubomir Bourdev, Ross Girshick, James Hays, Pietro Perona, Deva Ramanan, C. Lawrence Zitnick, Piotr Dollár

We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding.

Instance Segmentation Object Localization +4

PANDA: Pose Aligned Networks for Deep Attribute Modeling

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.

General Classification Object Recognition

Articulated Pose Estimation Using Discriminative Armlet Classifiers

no code implementations CVPR 2013 Georgia Gkioxari, Pablo Arbelaez, Lubomir Bourdev, Jitendra Malik

We propose a novel approach for human pose estimation in real-world cluttered scenes, and focus on the challenging problem of predicting the pose of both arms for each person in the image.

Pose Estimation

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