DeepProposal: Hunting Objects by Cascading Deep Convolutional Layers

Ghodrati, Amir; Diba, Ali; Pedersoli, Marco; Tuytelaars, Tinne; Gool, Luc Van

doi:10.1109/iccv.2015.296

Cited by 111 publications

(66 citation statements)

References 27 publications

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“…For example, network trained on COB proposals is denoted as "COB+OA". We compare COB+OA against nine state-of-the-art object proposal generators, including Objectness Measure [4], Contour Box [31], Selective Search [47], Edge Box [63], MCG [5], COB [34], Deep Box [24], Deep Proposal [16], and RPN [38]. The last four are the latest deep learning based methods.…”

Section: Comparison With State Of the Art Methodsmentioning

confidence: 99%

“…Instead of directly searching for optimal windows, we use off-the-shelves object proposal generators for proposal box generation. Several methods are tried, including Selective Search [47], Edge Box [63], MCG [5], RPN from Faster R-CNN [38], and Deep Proposal [16]. In this work, we prefer fast proposal generation methods with good precision so that later our assessment network can indeed find optimal object proposals.…”

Section: Object Databasementioning

confidence: 99%

See 1 more Smart Citation

Harvesting Visual Objects from Internet Images via Deep-Learning-Based Objectness Assessment

et al. 2019

ACM Trans. Multimedia Comput. Commun. Appl.

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The collection of internet images has been growing in an astonishing speed. It is undoubted that these images contain rich visual information that can be useful in many applications, such as visual media creation and data-driven image synthesis. In this paper, we focus on the methodologies for building a visual object database from a collection of internet images. Such database is built to contain a large number of high-quality visual objects that can help with various data-driven image applications. Our method is based on dense proposal generation and objectness-based re-ranking. A novel deep convolutional neural network is designed for the inference of proposal objectness, the probability of a proposal containing optimally-located foreground object. In our work, the objectness is quantitatively measured in regard of completeness and fullness, reflecting two complementary features of an optimal proposal: a complete foreground and relatively small background. Our experiments indicate that object proposals re-ranked according to the output of our network generally achieve higher performance than those produced by other state-of-the-art methods. As a concrete example, a database of over 1.2 million visual objects has been built using the proposed method, and has been successfully used in various data-driven image applications.

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Section: Comparison With State Of the Art Methodsmentioning

confidence: 99%

Section: Object Databasementioning

confidence: 99%

Harvesting Visual Objects from Internet Images via Deep-Learning-Based Objectness Assessment

et al. 2019

ACM Trans. Multimedia Comput. Commun. Appl.

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“…There is no exception in the task of object proposals [19,20,30]. Zhang et al [64] leveraged a Convolutional-Neural-Network model to generate location proposals of salient objects.…”

Section: Frame-based Object Proposals the Concept Of Object Proposalsmentioning

confidence: 98%

Adaptive video object proposals by a context-aware model

Geng

Zhang

2017

Multimed Tools Appl

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Most previous works focus on image object proposals while few on video object proposals. Besides, the existing explorations about video object proposals mainly concentrate on localizing the dominant object. In this paper, we aim at exploring a uniform framework for proposing multi-objects in videos no matter they are in the foreground or background. The method is derived from image object proposals, and makes best use of video characteristics. To achieve this task, we propose an adaptive context-aware model for video object proposals. First, spatial candidate windows are generated by the image method for acquiring the adequate bounding box samples. Temporal boxes are calculated by the motion based mapping. Considering the mapping loss, we define a box confidence coefficient contributing to keeping the proposal consistency and restraining the motion blur. The output proposal bounding boxes are ranked based on the scores calculated by the weighted scoring system. The proposed method is separately evaluated on the proposed multi-object dataset and the public dataset. The results compared with several state-of-the-arts show that our method has the most satisfactory overall performance for multi-object proposals in videos.

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“…MetaAnchor [32] introduces meta-learning to anchor generation. There have been attempts [8,9,23,31,33,34,1,2] that apply cascade architecture to reject easy samples at early layers or stages, and regress bounding boxes iteratively for progressive refinement. Compared to two-stage approaches, the single-stage pipeline skips object proposal generation and predicts bounding boxes and class scores in one evaluation.…”

Section: Related Workmentioning

confidence: 99%

Region Proposal by Guided Anchoring

Wang

Chen

Yang

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

561

302

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Region anchors are the cornerstone of modern object detection techniques. State-of-the-art detectors mostly rely on a dense anchoring scheme, where anchors are sampled uniformly over the spatial domain with a predefined set of scales and aspect ratios. In this paper, we revisit this foundational stage. Our study shows that it can be done much more effectively and efficiently. Specifically, we present an alternative scheme, named Guided Anchoring, which leverages semantic features to guide the anchoring. The proposed method jointly predicts the locations where the center of objects of interest are likely to exist as well as the scales and aspect ratios at different locations. On top of predicted anchor shapes, we mitigate the feature inconsistency with a feature adaption module. We also study the use of high-quality proposals to improve detection performance. The anchoring scheme can be seamlessly integrated into proposal methods and detectors. With Guided Anchoring, we achieve 9.1% higher recall on MS COCO with 90% fewer anchors than the RPN baseline. We also adopt Guided Anchoring in Fast R-CNN, Faster R-CNN and RetinaNet, respectively improving the detection mAP by 2.2%, 2.7% and 1.2%. Code will be available at https: //github.com/open-mmlab/mmdetection.

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DeepProposal: Hunting Objects by Cascading Deep Convolutional Layers

Cited by 111 publications

References 27 publications

Harvesting Visual Objects from Internet Images via Deep-Learning-Based Objectness Assessment

Harvesting Visual Objects from Internet Images via Deep-Learning-Based Objectness Assessment

Adaptive video object proposals by a context-aware model

Region Proposal by Guided Anchoring

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