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Found 6 papers, 1 papers with code
Split-Merge Pooling
There are a variety of approaches to obtain a vast receptive field with convolutional neural networks (CNNs), such as pooling or striding convolutions.
Deep Object Co-Segmentation
This work presents a deep object co-segmentation (DOCS) approach for segmenting common objects of the same class within a pair of images.
iPose: Instance-Aware 6D Pose Estimation of Partly Occluded Objects
We address the task of 6D pose estimation of known rigid objects from single input images in scenarios where the objects are partly occluded.
Bounding Boxes, Segmentations and Object Coordinates: How Important Is Recognition for 3D Scene Flow Estimation in Autonomous Driving Scenarios?
Existing methods for 3D scene flow estimation often fail in the presence of large displacement or local ambiguities, e. g., at texture-less or reflective surfaces.
Analyzing Modular CNN Architectures for Joint Depth Prediction and Semantic Segmentation
Towards this end we propose a Convolutional Neural Network (CNN) architecture that fuses the state of the state-of-the-art results for depth estimation and semantic labeling.
Real-Time RGB-D based Template Matching Pedestrian Detection
We show that our method outperforms the state-of-the-art approaches.
