Unsupervised Detection of Regions of Interest Using Iterative Link Analysis

By Gunhee Kim, Antonio Torralba. 2009

115 citations recorded by the source corpus.

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Citations in the corpus

Spatially Invariant Unsupervised Object Detection with Convolutional Neural Networks (2019, methodology): “Related work from computer vision includes (Cao and Fei-Fei 2007; Kim and Torralba 2009; Lee and Grauman 2009; Russell et al. 2006; Karpathy, Miller, and Fei-Fei 2013), as well as work on co-segmentation (Zhu et al. 2016) and unsupervised object recognition (Tuytelaars et al. 2010).”
Unsupervised Discovery of Mid-Level Discriminative Patches (2012, background): “…goals, our work is quite diﬀerent in that: 1) we do not explicitly aim to discover whole semantic units like objects or parts, 2) unlike [25,27,30], we do not assume a single object per image, 3) whereas in object discovery there is no separate training and test set, we explicitly aim to…”
Unsupervised Object Discovery and Tracking in Video Collections (2015, background): “Related approaches have been proposed for salient region detection [18], image cosegmentation [36, 37], and image colo-calization [4].”
Unsupervised object discovery and localization in the wild: Part-based matching with bottom-up region proposals (2015, methodology): “Region proposals have been used in many of the methods discussed so far, but most of them [12, 27, 28, 42, 51, 54] use relatively a small number of the best proposals (typically, less than 100 for each image) to form whole object hypotheses, often together with generic objectness measures [1].”
Toward unsupervised, multi-object discovery in large-scale image collections (2020, background): “out ne-tuning the feature extractor. Moreover, our region proposals come with a nice intrinsic structure which can be exploited to improve object discovery performance. Early work on object discovery [12,15,20,27,30] focused on a restricted setting where images are from only a few distinctive object”
Y Qualification Main task Y Return N N Y User qualified ? N Feedback Pass ? Pass ? Annotator training Annotating images (2017, methodology): “As initialization, in the first iteration we train the classifier using complete images as positive training examples [10, 11, 44, 54, 43, 30].”
Unsupervised Visual Representation Learning by Graph-Based Consistent Constraints (2016, methodology): “Existing methods use various forms of clustering or matching algorithms for object discovery [23], ROI detection [24] and patch mining [25,26].”
Weakly Supervised Localization and Learning with Generic Knowledge (2012, unknown): “We compare to various baselines and three existing meth-ods [Chum and Zisserman, 2007, Kim and Torralba, 2009, Russell et al, 2006].”
Spot On: Action Localization from Pointly-Supervised Proposals (2016, methodology): “We draw inspiration from related work on weakly-supervised object detection, e.g., [6,7,8].”
Key-segments for video object segmentation (2011, background): “Beyond single images, some work considers discovering repeated patterns among pairs or groups of unlabeled images [26, 13, 16].”
Discovering localized attributes for fine-grained recognition (2012, unknown): “However, these methods aim to ﬁnd objects that are common across images, while we are interested in ﬁnding discriminative local regions that will maximize classiﬁcation performance.”
Unsupervised Joint Object Discovery and Segmentation in Internet Images (2013, methodology): “Recently, PageRank [7] was used to discover regions of interest in a bounding box representation [10], and selfsimilarities were used to discover a common pattern in several images [1].”
ConceptMap: Mining Noisy Web Data for Concept Learning (2014, unknown): “[34] [50] [27] [50, 9, 8, 25, 10, 26, 35, 7].”
Cluster-based Co-saliency Detection (2013, unknown): “Existing approaches discover possible regions of object instances and output a set of windows containing the common object [50]–[52].”