Search Results for author:
Found 18 papers, 13 papers with code
Deep Continuous Networks
CNNs and computational models of biological vision share some fundamental principles, which opened new avenues of research.
A step towards understanding why classification helps regression
A number of computer vision deep regression approaches report improved results when adding a classification loss to the regression loss.
Is there progress in activity progress prediction?
We conclude that the progress prediction task is ill-posed on the currently used real-world datasets.
Objects do not disappear: Video object detection by single-frame object location anticipation
2) Improved efficiency by only doing the expensive feature computations on a small subset of all frames.
Ranked #1 on
Video Object Detection
on Waymo Open Dataset
Deep vanishing point detection: Geometric priors make dataset variations vanish
Deep learning has improved vanishing point detection in images.
Equal Bits: Enforcing Equally Distributed Binary Network Weights
We investigate experimentally that equal bit ratios are indeed preferable and show that our method leads to optimization benefits.
Frequency learning for structured CNN filters with Gaussian fractional derivatives
Frequency information lies at the base of discriminating between textures, and therefore between different objects.
Resolution learning in deep convolutional networks using scale-space theory
We use scale-space theory to obtain a self-similar parametrization of filters and make use of the N-Jet: a truncated Taylor series to approximate a filter by a learned combination of Gaussian derivative filters.
No frame left behind: Full Video Action Recognition
A common heuristic is uniformly sampling a small number of video frames and using these to recognize the action.
Deep Hough-Transform Line Priors
Here, we reduce the dependency on labeled data by building on the classic knowledge-based priors while using deep networks to learn features.
Ranked #3 on
Line Segment Detection
on wireframe dataset
Top-Down Networks: A coarse-to-fine reimagination of CNNs
Biological vision adopts a coarse-to-fine information processing pathway, from initial visual detection and binding of salient features of a visual scene, to the enhanced and preferential processing given relevant stimuli.
Hand-tremor frequency estimation in videos
We focus on the problem of estimating human hand-tremor frequency from input RGB video data.
Using phase instead of optical flow for action recognition
We design these complex filters to resemble complex Gabor filters, typically employed for phase-information extraction.
Recurrent knowledge distillation
Knowledge distillation compacts deep networks by letting a small student network learn from a large teacher network.
Deja Vu: Motion Prediction in Static Images
This paper proposes motion prediction in single still images by learning it from a set of videos.
Asymmetric kernel in Gaussian Processes for learning target variance
This enables each center to adjust the kernel space in its vicinity in correspondence with the topology of the targets --- a multi-modal approach.
Featureless: Bypassing feature extraction in action categorization
This method introduces an efficient manner of learning action categories without the need of feature estimation.
Video Acceleration Magnification
In these contexts there is often large motion present which severely distorts current video amplification methods that magnify change linearly.
