Search Results for author: Rogerio S. Feris

Found 12 papers, 3 papers with code

Everything at Once - Multi-Modal Fusion Transformer for Video Retrieval

1 code implementation CVPR 2022 Nina Shvetsova, Brian Chen, Andrew Rouditchenko, Samuel Thomas, Brian Kingsbury, Rogerio S. Feris, David Harwath, James Glass, Hilde Kuehne

In this work, we present a multi-modal, modality agnostic fusion transformer that learns to exchange information between multiple modalities, such as video, audio, and text, and integrate them into a fused representation in a joined multi-modal embedding space.

Action Localization Video Retrieval

Diversity in Faces

no code implementations29 Jan 2019 Michele Merler, Nalini Ratha, Rogerio S. Feris, John R. Smith

We expect face recognition to work equally accurately for every face.

Face Recognition Fairness

RED-Net: A Recurrent Encoder-Decoder Network for Video-based Face Alignment

no code implementations17 Jan 2018 Xi Peng, Rogerio S. Feris, Xiaoyu Wang, Dimitris N. Metaxas

We propose a novel method for real-time face alignment in videos based on a recurrent encoder-decoder network model.

Face Alignment

Automatic Curation of Golf Highlights using Multimodal Excitement Features

no code implementations22 Jul 2017 Michele Merler, Dhiraj Joshi, Quoc-Bao Nguyen, Stephen Hammer, John Kent, John R. Smith, Rogerio S. Feris

The production of sports highlight packages summarizing a game's most exciting moments is an essential task for broadcast media.

Action Recognition

A Recurrent Encoder-Decoder Network for Sequential Face Alignment

no code implementations19 Aug 2016 Xi Peng, Rogerio S. Feris, Xiaoyu Wang, Dimitris N. Metaxas

We propose a novel recurrent encoder-decoder network model for real-time video-based face alignment.

Face Alignment

An exploration of parameter redundancy in deep networks with circulant projections

no code implementations ICCV 2015 Yu Cheng, Felix X. Yu, Rogerio S. Feris, Sanjiv Kumar, Alok Choudhary, Shih-Fu Chang

We explore the redundancy of parameters in deep neural networks by replacing the conventional linear projection in fully-connected layers with the circulant projection.

Designing Category-Level Attributes for Discriminative Visual Recognition

no code implementations CVPR 2013 Felix X. Yu, Liangliang Cao, Rogerio S. Feris, John R. Smith, Shih-Fu Chang

In this paper, we propose a novel formulation to automatically design discriminative "category-level attributes", which can be efficiently encoded by a compact category-attribute matrix.

Transfer Learning Zero-Shot Learning

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