output
20022026
most citedFitNets: Hints for Thin Deep Nets

2k citations

Showing 2018 · cs.CVShow all

17 papers · 2 filters

cs.CV2018★ 25 cited

Deep Spectral Reflectance and Illuminant Estimation from Self-Interreflections

Rada Deeb, Joost Van De Weijer, Damien Muselet +2

In this work, we propose a CNN-based approach to estimate the spectral reflectance of a surface and the spectral power distribution of the light from a single RGB image of a V-shap…

cs.CV2018★ 18 cited

Optimizing speed/accuracy trade-off for person re-identification via knowledge distillation

Idoia Ruiz, Bogdan Raducanu, Rakesh Mehta +1

Finding a person across a camera network plays an important role in video surveillance. For a real-world person re-identification application, in order to guarantee an optimal time…

cs.CV2018★ 12 cited

Psychophysical evaluation of individual low-level feature influences on visual attention

David Berga, Xosé Ramón Fdez-Vidal, Xavier Otazu +2

In this study we provide the analysis of eye movement behavior elicited by low-level feature distinctiveness with a dataset of synthetically-generated image patterns. Design of vis…

cs.CV2018

On Offline Evaluation of Vision-based Driving Models

Felipe Codevilla, Antonio M. López, Vladlen Koltun +1

Autonomous driving models should ideally be evaluated by deploying them on a fleet of physical vehicles in the real world. Unfortunately, this approach is not practical for the vas…

cs.CV2018★ 1 cited

Single Shot Scene Text Retrieval

Lluís Gómez, Andrés Mafla, Marçal Rusiñol +1

Textual information found in scene images provides high level semantic information about the image and its context and it can be leveraged for better scene understanding. In this p…

cs.CV2018★ 9 cited

Joint Coarse-And-Fine Reasoning for Deep Optical Flow

Victor Vaquero, German Ros, Francesc Moreno-Noguer +2

We propose a novel representation for dense pixel-wise estimation tasks using CNNs that boosts accuracy and reduces training time, by explicitly exploiting joint coarse-and-fine re…