Anna Khoreva (PhD Student)

MSc Anna Khoreva

Max-Planck-Institut für Informatik
Saarland Informatics Campus
Campus E1 4
66123 Saarbrücken
E1 4 - Room 618
+49 681 9325 2118
+49 681 9325 1899
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Simple Does It: Weakly Supervised Instance and Semantic Segmentation
A. Khoreva, R. Benenson, J. Hosang, M. Hein and B. Schiele
30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017), 2017
(Accepted/in press)
Learning Video Object Segmentation from Static Images
A. Khoreva, F. Perazzi, R. Benenson, B. Schiele and A. Sorkine-Hornung
30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017), 2017
(Accepted/in press)
Exploiting Saliency for Object Segmentation from Image Level Labels
S. J. Oh, R. Benenson, A. Khoreva, Z. Akata, M. Fritz and B. Schiele
30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017), 2017
(Accepted/in press)
Lucid Data Dreaming for Object Tracking
A. Khoreva, R. Benenson, E. Ilg, T. Brox and B. Schiele
Technical Report, 2017
(arXiv: 1703.09554)
Convolutional networks reach top quality in pixel-level object tracking but require a large amount of training data (1k ~ 10k) to deliver such results. We propose a new training strategy which achieves state-of-the-art results across three evaluation datasets while using 20x ~ 100x less annotated data than competing methods. Instead of using large training sets hoping to generalize across domains, we generate in-domain training data using the provided annotation on the first frame of each video to synthesize ("lucid dream") plausible future video frames. In-domain per-video training data allows us to train high quality appearance- and motion-based models, as well as tune the post-processing stage. This approach allows to reach competitive results even when training from only a single annotated frame, without ImageNet pre-training. Our results indicate that using a larger training set is not automatically better, and that for the tracking task a smaller training set that is closer to the target domain is more effective. This changes the mindset regarding how many training samples and general "objectness" knowledge are required for the object tracking task.
Weakly Supervised Object Boundaries
A. Khoreva, R. Benenson, M. Omran, M. Hein and B. Schiele
29th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016), 2016
State-of-the-art learning based boundary detection methods require extensive training data. Since labelling object boundaries is one of the most expensive types of annotations, there is a need to relax the requirement to carefully annotate images to make both the training more affordable and to extend the amount of training data. In this paper we propose a technique to generate weakly supervised annotations and show that bounding box annotations alone suffice to reach high-quality object boundaries without using any object-specific boundary annotations. With the proposed weak supervision techniques we achieve the top performance on the object boundary detection task, outperforming by a large margin the current fully supervised state-of-the-art methods.
Improved Image Boundaries for Better Video Segmentation
A. Khoreva, R. Benenson, F. Galasso, M. Hein and B. Schiele
Computer Vision -- ECCV 2016 Workshops, 2016
Graph-based video segmentation methods rely on superpixels as starting point. While most previous work has focused on the construction of the graph edges and weights as well as solving the graph partitioning problem, this paper focuses on better superpixels for video segmentation. We demonstrate by a comparative analysis that superpixels extracted from boundaries perform best, and show that boundary estimation can be significantly improved via image and time domain cues. With superpixels generated from our better boundaries we observe consistent improvement for two video segmentation methods in two different datasets.
Classifier Based Graph Construction for Video Segmentation
A. Khoreva, F. Galasso, M. Hein and B. Schiele
IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2015), 2015
Learning Must-Link Constraints for Video Segmentation Based on Spectral Clustering
A. Khoreva, F. Galasso, M. Hein and B. Schiele
Pattern Recognition (GCPR 2014), 2014