@conference {Li2019,
	title = {Deep Object Co-segmentation},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {11363 LNCS},
	year = {2019},
	pages = {638{\textendash}653},
	abstract = {This work presents a deep object co-segmentation (DOCS) approach for segmenting common objects of the same class within a pair of images. This means that the method learns to ignore common, or uncommon, background stuff and focuses on common objects. If multiple object classes are presented in the image pair, they are jointly extracted as foreground. To address this task, we propose a CNN-based Siamese encoder-decoder architecture. The encoder extracts high-level semantic features of the foreground objects, a mutual correlation layer detects the common objects, and finally, the decoder generates the output foreground masks for each image. To train our model, we compile a large object co-segmentation dataset consisting of image pairs from the PASCAL dataset with common objects masks. We evaluate our approach on commonly used datasets for co-segmentation tasks and observe that our approach consistently outperforms competing methods, for both seen and unseen object classes.},
	isbn = {9783030208929},
	issn = {16113349},
	doi = {10.1007/978-3-030-20893-6_40},
	author = {Weihao Li and Omid Hosseini Jafari and Carsten Rother}
}
@conference {HosseiniJafari2019,
	title = {iPose: Instance-Aware 6D Pose Estimation of Partly Occluded Objects},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {11363 LNCS},
	year = {2019},
	pages = {477{\textendash}492},
	abstract = {We address the task of 6D pose estimation of known rigid objects from single input images in scenarios where the objects are partly occluded. Recent RGB-D-based methods are robust to moderate degrees of occlusion. For RGB inputs, no previous method works well for partly occluded objects. Our main contribution is to present the first deep learning-based system that estimates accurate poses for partly occluded objects from RGB-D and RGB input. We achieve this with a new instance-aware pipeline that decomposes 6D object pose estimation into a sequence of simpler steps, where each step removes specific aspects of the problem. The first step localizes all known objects in the image using an instance segmentation network, and hence eliminates surrounding clutter and occluders. The second step densely maps pixels to 3D object surface positions, so called object coordinates, using an encoder-decoder network, and hence eliminates object appearance. The third, and final, step predicts the 6D pose using geometric optimization. We demonstrate that we significantly outperform the state-of-the-art for pose estimation of partly occluded objects for both RGB and RGB-D input.},
	isbn = {9783030208929},
	issn = {16113349},
	doi = {10.1007/978-3-030-20893-6_30},
	author = {Omid Hosseini Jafari and Mustikovela, Siva Karthik and Pertsch, Karl and Brachmann, Eric and Carsten Rother}
}
@techreport {Li,
	title = {Localizing Common Objects Using Common Component Activation Map},
	year = {2019},
	abstract = {In this work, we propose an approach to localize common objects from novel object categories in a set of images. We solve this problem using a new common component activation map (CCAM) in which we treat the class-specific activation maps (CAM) as components to discover the common components in the image set. We show that our approach can generalize on novel object categories in our experiments .},
	author = {Weihao Li and Omid Hosseini Jafari and Carsten Rother}
}
@proceedings {6310,
	title = {iPose: Instance-Aware 6D Pose Estimation of Partly Occluded Objects},
	year = {2018},
	author = {Omid Hosseini Jafari and Mustikovela, S K and Pertsch, K and E Brachmann and Carsten Rother}
}
@conference {Jafari2017,
	title = {Analyzing modular CNN architectures for joint depth prediction and semantic segmentation},
	booktitle = {Proceedings - IEEE International Conference on Robotics and Automation},
	year = {2017},
	month = {feb},
	pages = {4620{\textendash}4627},
	abstract = {This paper addresses the task of designing a modular neural network architecture that jointly solves different tasks. As an example we use the tasks of depth estimation and semantic segmentation given a single RGB image. The main focus of this work is to analyze the cross-modality influence between depth and semantic prediction maps on their joint refinement. While most of the previous works solely focus on measuring improvements in accuracy, we propose a way to quantify the cross-modality influence. We show that there is a relationship between final accuracy and cross-modality influence, although not a simple linear one. Hence a larger cross-modality influence does not necessarily translate into an improved accuracy. We find that a beneficial balance between the cross-modality influences can be achieved by network architecture and conjecture that this relationship can be utilized to understand different network design choices. Towards this end we propose a Convolutional Neural Network (CNN) architecture that fuses the state-of-the-art results for depth estimation and semantic labeling. By balancing the cross-modality influences between depth and semantic prediction, we achieve improved results for both tasks using the NYU-Depth v2 benchmark.},
	isbn = {9781509046331},
	issn = {10504729},
	doi = {10.1109/ICRA.2017.7989537},
	url = {http://arxiv.org/abs/1702.08009 http://dx.doi.org/10.1109/ICRA.2017.7989537},
	author = {Omid Hosseini Jafari and Groth, Oliver and Kirillov, Alexander and Yang, Michael Ying and Carsten Rother}
}
@conference {Behl2017,
	title = {Bounding Boxes, Segmentations and Object Coordinates: How Important is Recognition for 3D Scene Flow Estimation in Autonomous Driving Scenarios?},
	booktitle = {Proceedings of the IEEE International Conference on Computer Vision},
	volume = {2017-Octob},
	year = {2017},
	pages = {2593{\textendash}2602},
	abstract = {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. However, these challenges are omnipresent in dynamic road scenes, which is the focus of this work. Our main contribution is to overcome these 3D motion estimation problems by exploiting recognition. In particular, we investigate the importance of recognition granularity, from coarse 2D bounding box estimates over 2D instance segmentations to fine-grained 3D object part predictions. We compute these cues using CNNs trained on a newly annotated dataset of stereo images and integrate them into a CRF-based model for robust 3D scene flow estimation - an approach we term Instance Scene Flow. We analyze the importance of each recognition cue in an ablation study and observe that the instance segmentation cue is by far strongest, in our setting. We demonstrate the effectiveness of our method on the challenging KITTI 2015 scene flow benchmark where we achieve state-of-the-art performance at the time of submission.},
	isbn = {9781538610329},
	issn = {15505499},
	doi = {10.1109/ICCV.2017.281},
	author = {Behl, Aseem and Omid Hosseini Jafari and Mustikovela, Siva Karthik and Hassan Abu Alhaija and Carsten Rother and Geiger, Andreas}
}
@conference {Behl2017a,
	title = {Bounding Boxes, Segmentations and Object Coordinates: How Important is Recognition for 3D Scene Flow Estimation in Autonomous Driving Scenarios?},
	booktitle = {Proceedings of the IEEE International Conference on Computer Vision},
	volume = {2017-Octob},
	year = {2017},
	pages = {2593{\textendash}2602},
	abstract = {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. However, these challenges are omnipresent in dynamic road scenes, which is the focus of this work. Our main contribution is to overcome these 3D motion estimation problems by exploiting recognition. In particular, we investigate the importance of recognition granularity, from coarse 2D bounding box estimates over 2D instance segmentations to fine-grained 3D object part predictions. We compute these cues using CNNs trained on a newly annotated dataset of stereo images and integrate them into a CRF-based model for robust 3D scene flow estimation - an approach we term Instance Scene Flow. We analyze the importance of each recognition cue in an ablation study and observe that the instance segmentation cue is by far strongest, in our setting. We demonstrate the effectiveness of our method on the challenging KITTI 2015 scene flow benchmark where we achieve state-of-the-art performance at the time of submission.},
	isbn = {9781538610329},
	issn = {15505499},
	doi = {10.1109/ICCV.2017.281},
	author = {Behl, Aseem and Omid Hosseini Jafari and Mustikovela, Siva Karthik and Hassan Abu Alhaija and Carsten Rother and Geiger, Andreas}
}
@article {Hullin2017,
	title = {Semantic-Aware Image Smoothing},
	journal = {Vision, Modeling, and Visualization},
	year = {2017},
	abstract = {Structure-preserving image smoothing aims to extract semantically meaningful image structure from texture, which is one of the fundamental problems in computer vision and graphics. However, it is still not clear how to define this concept. On the other hand, semantic image labeling has achieved significant progress recently and has been widely used in many computer vision tasks. In this paper, we present an interesting observation, i.e. high-level semantic image labeling information can provide a meaningful structure prior naturally. Based on this observation, we propose a simple and yet effective method, which we term semantic smoothing, by exploiting the semantic information to accomplish semantically structure-preserving image smoothing. We show that our approach outperforms the state-of-the-art approaches in texture removal by considering the semantic infor-mation for structure preservation. Also, we apply our approach to three applications: detail enhancement, edge detection, and image segmentation, and we demonstrate the effectiveness of our semantic smoothing method on these problems.},
	keywords = {Enhancement{\textemdash} Smoothing, I43 [Image Processing and Computer Vision]},
	url = {https://hci.iwr.uni-heidelberg.de/vislearn/wp-content/uploads/2014/08/paper1024_CRC.pdf},
	author = {Hullin, M and Klein, R and Schultz, T and Yao, A and Weihao Li and Omid Hosseini Jafari and Carsten Rother}
}
@conference {Jafari2016,
	title = {Real-time RGB-D based template matching pedestrian detection},
	booktitle = {Proceedings - IEEE International Conference on Robotics and Automation},
	volume = {2016-June},
	year = {2016},
	pages = {5520{\textendash}5527},
	abstract = {Pedestrian detection is one of the most popular topics in computer vision and robotics. Considering challenging issues in multiple pedestrian detection, we present a real-time depth-based template matching people detector. In this paper, we propose different approaches for training the depth-based template. We train multiple templates for handling issues due to various upper-body orientations of the pedestrians and different levels of detail in depth-map of the pedestrians with various distances from the camera. And, we take into account the degree of reliability for different regions of sliding window by proposing the weighted template approach. Furthermore, we combine the depth-detector with an appearance based detector as a verifier to take advantage of the appearance cues for dealing with the limitations of depth data. We evaluate our method on the challenging ETH dataset sequence. We show that our method outperforms the state-of-the-art approaches.},
	isbn = {9781467380263},
	issn = {10504729},
	doi = {10.1109/ICRA.2016.7487767},
	author = {Omid Hosseini Jafari and Yang, Michael Ying}
}
