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, left-most, 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 43.2 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.
As a bottom-up object detection method, our idea of grouping center and extreme points is related to Deformable Part Model @cite_13 . Our center point detector functions similarly with the root filter in DPM @cite_13 , and our four extreme points can be considered as a universal part decomposition for all categories. Instead of learning the part configuration, our predicted center and four extreme points have a geometry structure. And we use a state-of-the-art keypoint detection network instead of low-level image filters for part detection.
@cite_13
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In this paper we are interested in how semantic segmentation can help object detection. Towards this goal, we propose a novel deformable part-based model which exploits region-based segmentation algorithms that compute candidate object regions by bottom-up clustering followed by ranking of those regions. Our approach allows every detection hypothesis to select a segment (including void), and scores each box in the image using both the traditional HOG filters as well as a set of novel segmentation features. Thus our model blends'' between the detector and segmentation models. Since our features can be computed very efficiently given the segments, we maintain the same complexity as the original DPM. We demonstrate the effectiveness of our approach in PASCAL VOC 2010, and show that when employing only a root filter our approach outperforms Dalal & Triggs detector on all classes, achieving 13 higher average AP. When employing the parts, we outperform the original DPM in @math out of @math classes, achieving an improvement of 8 AP. Furthermore, we outperform the previous state-of-the-art on VOC 2010 test by 4 .