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20132026
most citedSemantic Foggy Scene Understanding with Synthetic Data

1.4k citations · 2.6k across the 155 of their papers we have counts for

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Showing 2019 · cs.CVShow all

5 papers · 2 filters

cs.CV2019

Efficient Video Semantic Segmentation with Labels Propagation and Refinement

Matthieu Paul, Christoph Mayer, Luc Van Gool +1

This paper tackles the problem of real-time semantic segmentation of high definition videos using a hybrid GPU / CPU approach. We propose an Efficient Video Segmentation(EVS) pipel…

cs.CV2019

Towards Partial Supervision for Generic Object Counting in Natural Scenes

Hisham Cholakkal, Guolei Sun, Salman Khan +3

Generic object counting in natural scenes is a challenging computer vision problem. Existing approaches either rely on instance-level supervision or absolute count information to t…

cs.CV2019

Convex Relaxations for Consensus and Non-Minimal Problems in 3D Vision

Thomas Probst, Danda Pani Paudel, Ajad Chhatkuli +1

In this paper, we formulate a generic non-minimal solver using the existing tools of Polynomials Optimization Problems (POP) from computational algebraic geometry. The proposed met…

cs.CV2019★ 7 cited

Fast video object segmentation with Spatio-Temporal GANs

Sergi Caelles, Albert Pumarola, Francesc Moreno-Noguer +2

Learning descriptive spatio-temporal object models from data is paramount for the task of semi-supervised video object segmentation. Most existing approaches mainly rely on models…

cs.CV2019

Curriculum Model Adaptation with Synthetic and Real Data for Semantic Foggy Scene Understanding

Dengxin Dai, Christos Sakaridis, Simon Hecker +1

This work addresses the problem of semantic scene understanding under fog. Although marked progress has been made in semantic scene understanding, it is mainly concentrated on clea…