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Objects as Points
TL;DR: The center point based approach, CenterNet, is end-to-end differentiable, simpler, faster, and more accurate than corresponding bounding box based detectors and performs competitively with sophisticated multi-stage methods and runs in real-time.
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Abstract: Detection identifies objects as axis-aligned boxes in an image. Most successful object detectors enumerate a nearly exhaustive list of potential object locations and classify each. This is wasteful, inefficient, and requires additional post-processing. In this paper, we take a different approach. We model an object as a single point --- the center point of its bounding box. Our detector uses keypoint estimation to find center points and regresses to all other object properties, such as size, 3D location, orientation, and even pose. Our center point based approach, CenterNet, is end-to-end differentiable, simpler, faster, and more accurate than corresponding bounding box based detectors. CenterNet achieves the best speed-accuracy trade-off on the MS COCO dataset, with 28.1% AP at 142 FPS, 37.4% AP at 52 FPS, and 45.1% AP with multi-scale testing at 1.4 FPS. We use the same approach to estimate 3D bounding box in the KITTI benchmark and human pose on the COCO keypoint dataset. Our method performs competitively with sophisticated multi-stage methods and runs in real-time.
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Citations
•Posted Content
End-to-End Object Detection with Transformers
Nicolas Carion,Francisco Massa,Gabriel Synnaeve,Nicolas Usunier,Alexander Kirillov,Sergey Zagoruyko +5 more
TL;DR: This work presents a new method that views object detection as a direct set prediction problem, and demonstrates accuracy and run-time performance on par with the well-established and highly-optimized Faster RCNN baseline on the challenging COCO object detection dataset.
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End-to-End Object Detection with Transformers
Nicolas Carion,Francisco Massa,Gabriel Synnaeve,Nicolas Usunier,Alexander Kirillov,Sergey Zagoruyko +5 more
- 23 Aug 2020
TL;DR: DetR as mentioned in this paper proposes a set-based global loss that forces unique predictions via bipartite matching, and a transformer encoder-decoder architecture to directly output the final set of predictions in parallel.
8.5K
EfficientDet: Scalable and Efficient Object Detection
Mingxing Tan,Ruoming Pang,Quoc V. Le +2 more
- 14 Jun 2020
TL;DR: EfficientDetD7 as discussed by the authors proposes a weighted bi-directional feature pyramid network (BiFPN), which allows easy and fast multi-scale feature fusion, and a compound scaling method that uniformly scales the resolution, depth, and width for all backbone, feature network, and box/class prediction networks at the same time.
CSPNet: A New Backbone that can Enhance Learning Capability of CNN
Chien-Yao Wang,Hong-Yuan Mark Liao,Yueh-Hua Wu,Ping-Yang Chen,Jun-Wei Hsieh,I-Hau Yeh +5 more
- 14 Jun 2020
TL;DR: Cross Stage Partial Network (CSPNet) as discussed by the authors integrates feature maps from the beginning and the end of a network stage to mitigate the problem of duplicate gradient information within network optimization.
YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
TL;DR: YOLOv7 surpasses all known object detectors in both speed and accuracy in the range from 5 FPS to 160 FPS and has the highest accuracy 56.8% AP among all known real-time object detectors with 30 FPS or higher on GPU V100.
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•Posted Content
Deep Residual Learning for Image Recognition
TL;DR: This work presents a residual learning framework to ease the training of networks that are substantially deeper than those used previously, and provides comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth.
117.9K
Microsoft COCO: Common Objects in Context
Tsung-Yi Lin,Michael Maire,Serge Belongie,James Hays,Pietro Perona,Deva Ramanan,Piotr Dollár,C. Lawrence Zitnick +7 more
- 06 Sep 2014
TL;DR: A new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding by gathering images of complex everyday scenes containing common objects in their natural context.
You Only Look Once: Unified, Real-Time Object Detection
Joseph Redmon,Santosh K. Divvala,Ross Girshick,Ali Farhadi +3 more
- 27 Jun 2016
TL;DR: Compared to state-of-the-art detection systems, YOLO makes more localization errors but is less likely to predict false positives on background, and outperforms other detection methods, including DPM and R-CNN, when generalizing from natural images to other domains like artwork.