RESEARCH

COMPUTER VISION

Listen to Look: Action Recognition by Previewing Audio

June 14, 2020

Abstract

In the face of the video data deluge, today’s expensive clip-level classifiers are increasingly impractical. We propose a framework for efficient action recognition in untrimmed video that uses audio as a preview mechanism to eliminate both short-term and long-term visual redundancies. First, we devise an IMGAUD2VID framework that hallucinates clip-level features by distilling from lighter modalities—a single frame and its accompanying audio — reducing short-term temporal redundancy for efficient clip-level recognition. Second, building on IMGAUD2VID, we further propose IMGAUD-SKIMMING, an attention-based long short-term memory network that iteratively selects useful moments in untrimmed videos, reducing long-term temporal redundancy for efficient video-level recognition. Extensive experiments on four action recognition datasets demonstrate that our method achieves the state-of-the-art in terms of both recognition accuracy and speed.

Download the Paper

AUTHORS

Written by

Ruohan Gao

Tae-Hyun Oh

Kristen Grauman

Lorenzo Torresani

Publisher

Conference on Computer Vision and Pattern Recognition (CVPR)

Research Topics

Computer Vision

Related Publications

June 17, 2019

COMPUTER VISION

Graph-Based Global Reasoning Networks | Facebook AI Research

Globally modeling and reasoning over relations between regions can be beneficial for many computer vision tasks on both images and videos. Convolutional Neural Networks (CNNs) excel at modeling local relations by convolution operations, but…

Yunpeng Chen, Marcus Rohrbach, Zhicheng Yan, Shuicheng Yan, Jiashi Feng, Yannis Kalantidis

June 17, 2019

June 17, 2019

COMPUTER VISION

DMC-Net: Generating Discriminative Motion Cues for Fast Compressed Video Action Recognition | Facebook AI Research

Motion has shown to be useful for video understanding, where motion is typically represented by optical flow. However, computing flow from video frames is very time-consuming. Recent works directly leverage the motion vectors and residuals…

Zheng Shou, Xudong Lin, Yannis Kalantidis, Laura Sevilla-Lara, Marcus Rohrbach, Shih-Fu Chang, Zhicheng Yan

June 17, 2019

June 18, 2019

COMPUTER VISION

Embodied Question Answering in Photorealistic Environments with Point Cloud Perception | Facebook AI Research

To help bridge the gap between internet vision-style problems and the goal of vision for embodied perception we instantiate a large-scale navigation task – Embodied Question Answering [1] in photo-realistic environments (Matterport 3D). We…

Erik Wijmans, Samyak Datta, Oleksandr Maksymets, Abhishek Das, Georgia Gkioxari, Stefan Lee, Irfan Essa, Devi Parikh, Dhruv Batra

June 18, 2019

June 11, 2019

NLP

COMPUTER VISION

Adversarial Inference for Multi-Sentence Video Description | Facebook AI Research

While significant progress has been made in the image captioning task, video description is still in its infancy due to the complex nature of video data. Generating multi-sentence descriptions for long videos is even more challenging. Among the…

Jae Sung Park, Marcus Rohrbach, Trevor Darrell, Anna Rohrbach

June 11, 2019

Help Us Pioneer The Future of AI

We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.