*The location-sensitive visual recognition tasks, including object detection, instance segmentation, and human pose estimation, can be formulated into localizing an anchor point (in red) and a set of landmarks (in green). Our work aims to offer a unified framework for these tasks.*
## Abstract
Object detection, instance segmentation, and pose estimation are popular visual recognition tasks which require localizing the object by internal or boundary landmarks. This paper summarizes these tasks as location-sensitive visual recognition and proposes a unified solution named location-sensitive network (LSNet). Based on a deep neural network as the backbone, LSNet predicts an anchor point and a set of landmarks which together define the shape of the target object. The key to optimizing the LSNet lies in the ability of fitting various scales, for which we design a novel loss function named cross-IOU loss that computes the cross-IOU of each anchor-landmark pair to approximate the global IOU between the prediction and groundtruth. The flexibly located and accurately predicted landmarks also enable LSNet to incorporate richer contextual information for visual recognition. Evaluated on the MSCOCO dataset, LSNet set the new state-of-the-art accuracy for anchor-free object detection (a 53.5% box AP) and instance segmentation (a 40.2% mask AP), and shows promising performance in detecting multi-scale human poses.
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## Bbox AP(%) on COCO test-dev
|Method | Backbone | epoch | MS
*Some location-sensitive visual recognition results on the MS-COCO validation set.*
*We compared with the CenterNet to show that our LSNet w/ ‘obj-box’ tends to predict more human pose of small scales, which are not annotated on the dataset. Only pose results with scores higher than 0:3 are shown for both methods.*
*Left: LSNet uses the object bounding boxes to assign training samples. Right: LSNet uses the keypoint-boxes to
assign training samples. Although LSNet with keypoint-boxes enjoys higher AP score, its ability of perceiving multi-scale
human instances is weakened.*