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20162022
most citedSemantic Instance Segmentation with a Discriminative Loss Function

444 citations · 1.6k across the 80 of their papers we have counts for

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Showing 2016Show all

8 papers · 1 filter

cs.CV2016

DeepProposals: Hunting Objects and Actions by Cascading Deep Convolutional Layers

Amir Ghodrati, Ali Diba, Marco Pedersoli +2

In this paper, a new method for generating object and action proposals in images and videos is proposed. It builds on activations of different convolutional layers of a pretrained…

cs.LG2016

Dynamic Filter Networks

Bert De Brabandere, Xu Jia, Tinne Tuytelaars +1

In a traditional convolutional layer, the learned filters stay fixed after training. In contrast, we introduce a new framework, the Dynamic Filter Network, where filters are genera…

cs.CV2016

Low-Cost Scene Modeling using a Density Function Improves Segmentation Performance

Vivek Sharma, Sule Yildirim-Yayilgan, Luc Van Gool

We propose a low cost and effective way to combine a free simulation software and free CAD models for modeling human-object interaction in order to improve human & object segmentat…

cs.CV2016

Image-level Classification in Hyperspectral Images using Feature Descriptors, with Application to Face Recognition

Vivek Sharma, Luc Van Gool

In this paper, we proposed a novel pipeline for image-level classification in the hyperspectral images. By doing this, we show that the discriminative spectral information at image…

cs.CV2016

Actionness Estimation Using Hybrid Fully Convolutional Networks

Limin Wang, Yu Qiao, Xiaoou Tang +1

Actionness was introduced to quantify the likelihood of containing a generic action instance at a specific location. Accurate and efficient estimation of actionness is important in…

cs.CV2016

Direction matters: hand pose estimation from local surface normals

Chengde Wan, Angela Yao, Luc Van Gool

We present a hierarchical regression framework for estimating hand joint positions from single depth images based on local surface normals. The hierarchical regression follows the…