@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}
}
@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}
}
@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}
}
