Weakly Supervised Object Localization with Progressive Domain Adaptation Supplementary Material
نویسندگان
چکیده
In this supplementary material, we present three additional results to complement the paper. First, we report detailed quantitative evaluation on the PASCAL VOC and ILSVRC object detection datasets. Second, we show additional qualitative detection results on the VOC 2007 dataset. Third, we analyze the errors of three variants of the proposed approach and show relative contributions from each component.
منابع مشابه
Cross-Domain Weakly-Supervised Object Detection through Progressive Domain Adaptation
Can we detect common objects in a variety of image domains without instance-level annotations? In this paper, we present a framework for a novel task, cross-domain weakly supervised object detection, which addresses this question. For this paper, we have access to images with instance-level annotations in a source domain (e.g., natural image) and images with image-level annotations in a target ...
متن کاملProgressive Representation Adaptation for Weakly Supervised Object Localization
We address the problem of weakly supervised object localization where only image-level annotations are available for training object detectors. Numerous methods have been proposed to tackle this problem through mining object proposals. However, a substantial amount of noise in object proposals causes ambiguities for learning discriminative object models. Such approaches are sensitive to model i...
متن کاملWeakly Supervised Object Boundaries Supplementary material
In this work we propose to train boundary detectors using weakly supervised annotations. We propose and evaluate multiple strategies to generate annotations fusing different sources, such as unsupervised image segmentation [2], object proposal methods [10, 5], and object detectors [3, 6] (trained on bounding boxes). Figure 5 illustrates the examples of the proposed weakly supervised boundary an...
متن کاملSelf-Transfer Learning for Fully Weakly Supervised Object Localization
Recent advances of deep learning have achieved remarkable performances in various challenging computer vision tasks. Especially in object localization, deep convolutional neural networks outperform traditional approaches based on extraction of data/task-driven features instead of handcrafted features. Although location information of regionof-interests (ROIs) gives good prior for object localiz...
متن کاملC-WSL: Count-guided Weakly Supervised Localization
We introduce a count-guided weakly supervised localization (C-WSL) framework with per-class object count as an additional form of image-level supervision to improve weakly supervised localization (WSL). C-WSL uses a simple count-based region selection algorithm to select highquality regions, each of which covers a single object instance at training time, and improves WSL by training with the se...
متن کامل