CVPR 2014 Video Spotlights
TechTalks from event: CVPR 2014 Video Spotlights
Orals 2F : Convolutional Neural Networks
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Large-scale Video Classification with Convolutional Neural NetworksWe propose `filter forests' (FF), an efficient new discriminative approach for predicting continuous variables given a signal and its context. FF can be used for general signal restoration tasks that can be tackled via convolutional filtering, where it attempts to learn the optimal filtering kernels to be applied to each data point. The model can learn both the size of the kernel and its values, conditioned on the observation and its spatial or temporal context. We show that FF compares favorably to both Markov random field based and recently proposed regression forest based approaches for labeling problems in terms of efficiency and accuracy. In particular, we demonstrate how FF can be used to learn optimal denoising filters for natural images as well as for other tasks such as depth image refinement, and 1D signal magnitude estimation. Numerous experiments and quantitative comparisons show that FFs achieve accuracy at par or superior to recent state of the art techniques, while being several orders of magnitude faster.
- All Sessions
- Orals 1A : Matching & Reconstruction
- Orals 1B : Segmentation & Grouping
- Posters 1A : Recognition, Segmentation, Stereo & SFM
- Orals 1C : Statistical Methods & Learning I
- Orals 1D : Action Recognition
- Posters 1B : 3D Vision, Action Recognition, Recognition, Statistical Methods & Learning
- Orals 2A : Motion & Tracking
- Orals 2B : Discrete Optimization
- Posters 2A : Motion & Tracking, Optimization, Statistical Methods & Learning, Stereo & SFM
- Posters 2B : Face & Gesture, Recognition
- Orals 3A : Physics-Based Vision & Shape-from-X
- Orals 3B : Video: Events, Activities & Surveillance
- Posters 3A : Physics-Based Vision, Recognition, Video: Events, Activities & Surveillance
- Orals 3C : Medical & Biological Image Analysis
- Orals 3D : Low-Level Vision & Image Processing
- Posters 3B : Biologically Inspired Vision, Low-Level Vision, Medical & Biological Image Analysis, Segmentation
- Orals 4A : Computational Photography: Sensing and Display
- Orals 4B : Recognition: Detection, Categorization, Classification
- Posters 4A : Computational Photography, Motion & Tracking, Recognition
- Orals 4C : 3D Geometry & Shape
- Orals 4F : View Synthesis & Other Applications
- Posters 4B : 3D Vision, Document Analysis, Optimization Methods, Shape, Vision for Graphics, Web & Vision Systems
- Orals 2F : Convolutional Neural Networks