DeNet: Scalable Real-Time Object Detection with Directed Sparse Sampling

Tychsen-Smith, Lachlan; Petersson, Lars

doi:10.1109/iccv.2017.54

Cited by 131 publications

(72 citation statements)

References 17 publications

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“…Corner-Net achieved a 42.1% AP on MS COCO, outperforming all previous one stage detectors; however, the average inference time is 8 Boxes of various sizes and aspect ratios that serve as object candidates. 9 The idea of using keypoints for object detection appeared previously in DeNet [269]. about 4FPS on a Titan X GPU, significantly slower than SSD [175] and YOLO [227].…”

Section: Unified (One Stage) Frameworkmentioning

confidence: 99%

Deep Learning for Generic Object Detection: A Survey

et al. 2019

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Object detection, one of the most fundamental and challenging problems in computer vision, seeks to locate object instances from a large number of predefined categories in natural images. Deep learning techniques have emerged as a powerful strategy for learning feature representations directly from data and have led to remarkable breakthroughs in the field of generic object detection. Given this period of rapid evolution, the goal of this paper is to provide a comprehensive survey of the recent achievements in this field brought about by deep learning techniques. More than 300 research contributions are included in this survey, covering many aspects of generic object detection: detection frameworks, object feature representation, object proposal generation, context modeling, training strategies, and evaluation metrics. We finish the survey by identifying promising directions for future research.

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Section: Unified (One Stage) Frameworkmentioning

confidence: 99%

Deep Learning for Generic Object Detection: A Survey

et al. 2019

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“…Faster RCNN [41] further replaces region proposals [47] with a Region Proposal Network. The detection-by-classification idea is intuitive and keeps the best performance so far [6,7,19,20,24,27,37,45,46,54].…”

Section: Related Workmentioning

confidence: 99%

Bottom-Up Object Detection by Grouping Extreme and Center Points

Zhou

Zhuo

Krähenbühl

2019

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

942

477

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With the advent of deep learning, object detection drifted from a bottom-up to a top-down recognition problem. State of the art algorithms enumerate a near-exhaustive list of object locations and classify each into: object or not. In this paper, we show that bottom-up approaches still perform competitively. We detect four extreme points (top-most, leftmost, bottom-most, right-most) and one center point of objects using a standard keypoint estimation network. We group the five keypoints into a bounding box if they are geometrically aligned. Object detection is then a purely appearance-based keypoint estimation problem, without region classification or implicit feature learning. The proposed method performs on-par with the state-of-the-art region based detection methods, with a bounding box AP of 43.7% on COCO test-dev. In addition, our estimated extreme points directly span a coarse octagonal mask, with a COCO Mask AP of 18.9%, much better than the Mask AP of vanilla bounding boxes. Extreme point guided segmentation further improves this to 34.6% Mask AP.

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“…This adaptive and differentiable representation can be coherently used across the different stages of a modern object detector, and does not require the use of anchors to sample over a space of bounding boxes. RepPoints differs from existing non-rectangular representations for object detection, which are all built in a bottom-up manner [38,21,48]. These bottom-up representations identify individual points (e.g., bounding box corners or object extremities) and rely on handcrafted clustering to group them into object models.…”

Section: Introductionmentioning

confidence: 99%

RepPoints: Point Set Representation for Object Detection

Yang

Liu

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

890

432

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Modern object detectors rely heavily on rectangular bounding boxes, such as anchors, proposals and the final predictions, to represent objects at various recognition stages. The bounding box is convenient to use but provides only a coarse localization of objects and leads to a correspondingly coarse extraction of object features. In this paper, we present RepPoints (representative points), a new finer representation of objects as a set of sample points useful for both localization and recognition. Given ground truth localization and recognition targets for training, RepPoints learn to automatically arrange themselves in a manner that bounds the spatial extent of an object and indicates semantically significant local areas. They furthermore do not require the use of anchors to sample a space of bounding boxes. We show that an anchor-free object detector based on RepPoints can be as effective as the state-of-the-art anchor-based detection methods, with 46.5 AP and 67.4 AP 50 on the COCO test-dev detection benchmark, using ResNet-101 model.

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DeNet: Scalable Real-Time Object Detection with Directed Sparse Sampling

Cited by 131 publications

References 17 publications

Deep Learning for Generic Object Detection: A Survey

Deep Learning for Generic Object Detection: A Survey

Bottom-Up Object Detection by Grouping Extreme and Center Points

RepPoints: Point Set Representation for Object Detection

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