Learning to Co-Generate Object Proposals with a Deep Structured Network

Hayder, Zeeshan; He, Xuming; Salzmann, Mathieu

doi:10.1109/cvpr.2016.281

Cited by 13 publications

(7 citation statements)

References 22 publications

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“…Figure 7 illustrates recall under different IoU thresholds and number of proposals. Our algorithm is superior than or on par with previous state-of-the-arts, including: BING (Cheng et al, 2014), EdgeBox (Zitnick and Dollar, 2014), GOP (Krahenbuhl and Koltun, 2014), Selective Search (Uijlings et al, 2013), MCG (Arbeláez et al, 2014), Endres (Endres and Hoiem, 2014), Prims (Manén et al, 2013), Rigor (Humayun et al, 2014), Faster RCNN (Ren et al, 2015), AttractioNet (Gidaris and Komodakis, 2016), DeepBox (Kuo et al, 2015), CoGen (Hayder et al, 2016) (Pinheiro et al, 2016), and FPN (Lin et al, 2017). Table 3 reports the average recall vs. the number of proposals (from 10 to 1000) and the size of objects on ILSVRC.…”

Section: Average Recall For Region Proposalsmentioning

confidence: 99%

“…In (Krahenbuhl and Koltun, 2015), a learning method is proposed by training an ensemble of figure-ground segmentation models jointly, where individual models can specialize and complement each other. In recent years, CNN-based approaches (Hayder et al, 2016;Ghodrati et al, 2016;Pont-Tuset and Gool, 2015;He and Lau, 2015) are more popular with a nontrivial margin of performance boost. Jie et al (Jie et al, 2016) proposed a scale-aware pixel-wise proposal framework where two separate networks are learned to handle large and small objects, respectively.…”

Section: Related Workmentioning

confidence: 99%

“…Object proposal is the task of proposing a set of candidate regions or bounding boxes in an image that may potentially contain an object. In recent years, the emergence of object proposal algorithms (Uijlings et al, 2013;Manén et al, 2013;Arbeláez et al, 2014;Hayder et al, 2016;Kong et al, 2016;Ghodrati et al, 2016;Chavali et al, 2016;Sun et al, 2016; have significantly boosted the development of many vision tasks, (Liu et al, 2017a,b;Li et al, 2016;Chi et al, 2016;, especially for object detection (Girshick et al, 2014;Dai et al, 2016;Girshick, 2015;Bell et al, 2016;Liu et al, 2016). It is verified by Hosang et.al (Hosang et al, 2015) that region proposals with high average recall correlates well with good performance of a detector.…”

Section: Introductionmentioning

confidence: 98%

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Zoom Out-and-In Network with Map Attention Decision for Region Proposal and Object Detection

Liu

Ouyang

et al. 2018

Int J Comput Vis

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In this paper, we propose a zoom-out-and-in network for generating object proposals. A key observation is that it is difficult to classify anchors of different sizes with the same set of features. Anchors of different sizes should be placed accordingly based on different depth within a network: smaller boxes on high-resolution layers with a smaller stride while larger boxes on low-resolution counterparts with a larger stride. Inspired by the conv/deconv structure, we fully leverage the low-level local details and high-level regional semantics from two feature map streams, which are complimentary to each other, to identify the objectness in an image. A map attention decision (MAD) unit is further proposed to aggressively search for neuron activations among two streams and attend the most contributive ones on the feature learning of the final loss. The unit serves as a decisionmaker to adaptively activate maps along certain channels with the solely purpose of optimizing the overall training loss. One advantage of MAD is that the learned weights enforced on each feature channel is predicted on-the-fly based on the input context, which is more suitable than the fixed enforcement of a convolutional kernel. Experimental results on three datasets, including PASCAL VOC 2007, ImageNet DET, MS COCO, demonstrate the effectiveness of our proposed algorithm over other state-of-the-arts, in terms of average recall (AR) for region proposal and average precision (AP) for object detection.

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Section: Average Recall For Region Proposalsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

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Zoom Out-and-In Network with Map Attention Decision for Region Proposal and Object Detection

Liu

Ouyang

et al. 2018

Int J Comput Vis

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“…Kong et al [29] presented a deep hierarchical network for handling region proposal generation and object detection jointly. Hayder et al [23] proposed an approach to co-generate object proposals in multiple images by introducing a deep structured network that jointly predicted the objectness scores and the bounding box locations of multiple object candidates. Though most of these methods have achieved pleasing results, Chavali et al [7] reported the gameability of the current object proposal evaluation protocol especially for learning-based methods, for they argued that the choice of using a partially annotated dataset for evaluation of object proposals is problematic.…”

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

confidence: 99%

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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“…In this work we focus on the problem of generating bounding box object proposals rather than instance segmentations. Several approaches have been proposed in the literature for this task [1,2,5,6,8,18,25,26,29,40,44]. Among them our work is most related to the CNN-based objectness scoring approaches [12,27,33] that recently have demonstrated state-of-the-art results [32,33].…”

Section: Introductionmentioning

confidence: 99%

Attend Refine Repeat: Active Box Proposal Generation via In-Out Localization

Gidaris¹,

Komodakis²

2016

Procedings of the British Machine Vision Conference 2016

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The problem of computing category agnostic bounding box proposals is utilized as a core component in many computer vision tasks and thus has lately attracted a lot of attention. In this work we propose a new approach to tackle this problem that is based on an active strategy for generating box proposals that starts from a set of seed boxes, which are uniformly distributed on the image, and then progressively moves its attention on the promising image areas where it is more likely to discover well localized bounding box proposals. We call our approach AttractioNet and a core component of it is a CNN-based category agnostic object location refinement module that is capable of yielding accurate and robust bounding box predictions regardless of the object category.We extensively evaluate our AttractioNet approach on several image datasets (i.e. COCO, PASCAL, ImageNet detection and NYU-Depth V2 datasets) reporting on all of them state-of-the-art results that surpass the previous work in the field by a significant margin and also providing strong empirical evidence that our approach is capable to generalize to unseen categories. Furthermore, we evaluate our AttractioNet proposals in the context of the object detection task using a VGG16-Net based detector and the achieved detection performance on COCO manages to significantly surpass all other VGG16-Net based detectors while even being competitive with a heavily tuned ResNet-101 based detector. Code as well as box proposals computed for several datasets are available at:: https://github.com/gidariss/AttractioNet.

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Learning to Co-Generate Object Proposals with a Deep Structured Network

Cited by 13 publications

References 22 publications

Zoom Out-and-In Network with Map Attention Decision for Region Proposal and Object Detection

Zoom Out-and-In Network with Map Attention Decision for Region Proposal and Object Detection

Adaptive video object proposals by a context-aware model

Attend Refine Repeat: Active Box Proposal Generation via In-Out Localization

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