Location-Sensitive Visual Recognition with Cross-IOU Loss

• URL: http://arxiv.org/abs/2104.04899v1
• Date: Sun, 11 Apr 2021 02:17:14 GMT
• Title: Location-Sensitive Visual Recognition with Cross-IOU Loss
• Authors: Kaiwen Duan, Lingxi Xie, Honggang Qi, Song Bai, Qingming Huang and Qi Tian
• Abstract summary: This paper proposes a unified solution named location-sensitive network (LSNet) for object detection, instance segmentation, and pose estimation. 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.
• Score: 177.86369890708457
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 point-landmark pair to approximate the global IOU between the prediction and ground-truth. The flexibly located and accurately predicted landmarks also enable LSNet to incorporate richer contextual information for visual recognition. Evaluated on the MS-COCO 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. Code is available at https://github.com/Duankaiwen/LSNet

Related papers

• ELGC-Net: Efficient Local-Global Context Aggregation for Remote Sensing Change Detection [65.59969454655996]
  We propose an efficient change detection framework, ELGC-Net, which leverages rich contextual information to precisely estimate change regions. Our proposed ELGC-Net sets a new state-of-the-art performance in remote sensing change detection benchmarks. We also introduce ELGC-Net-LW, a lighter variant with significantly reduced computational complexity, suitable for resource-constrained settings.
  arXiv  Detail & Related papers  (2024-03-26T17:46:25Z)
• LSKNet: A Foundation Lightweight Backbone for Remote Sensing [78.29112381082243]
  This paper proposes a lightweight Large Selective Kernel Network (LSKNet) backbone. LSKNet can adjust its large spatial receptive field to better model the ranging context of various objects in remote sensing scenarios. Without bells and whistles, our lightweight LSKNet sets new state-of-the-art scores on standard remote sensing classification, object detection and semantic segmentation benchmarks.
  arXiv  Detail & Related papers  (2024-03-18T12:43:38Z)
• AGO-Net: Association-Guided 3D Point Cloud Object Detection Network [86.10213302724085]
  We propose a novel 3D detection framework that associates intact features for objects via domain adaptation. We achieve new state-of-the-art performance on the KITTI 3D detection benchmark in both accuracy and speed.
  arXiv  Detail & Related papers  (2022-08-24T16:54:38Z)
• Fast Camouflaged Object Detection via Edge-based Reversible Re-calibration Network [17.538512222905087]
  This paper proposes a novel edge-based reversible re-calibration network called ERRNet. Our model is characterized by two innovative designs, namely Selective Edge Aggregation (SEA) and Reversible Re-calibration Unit (RRU) Experimental results show that ERRNet outperforms existing cutting-edge baselines on three COD datasets and five medical image segmentation datasets.
  arXiv  Detail & Related papers  (2021-11-05T02:03:54Z)
• AdaCon: Adaptive Context-Aware Object Detection for Resource-Constrained Embedded Devices [2.5345835184316536]
  Convolutional Neural Networks achieve state-of-the-art accuracy in object detection tasks. They have large computational and energy requirements that challenge their deployment on resource-constrained edge devices. In this paper, we leverage the prior knowledge about the probabilities that different object categories can occur jointly to increase the efficiency of object detection models. Our experiments using COCO dataset show that our adaptive object detection model achieves up to 45% reduction in the energy consumption, and up to 27% reduction in the latency, with a small loss in the average precision (AP) of object detection.
  arXiv  Detail & Related papers  (2021-08-16T01:21:55Z)
• Decoupled Self Attention for Accurate One Stage Object Detection [4.791635488070342]
  A decoupled self attention(DSA) module is proposed for one stage object detection models in this paper. Although the network of DSA module is simple, but it can effectively improve the performance of object detection, also it can be easily embedded in many detection models.
  arXiv  Detail & Related papers  (2020-12-14T15:19:30Z)
• SOE-Net: A Self-Attention and Orientation Encoding Network for Point Cloud based Place Recognition [50.9889997200743]
  We tackle the problem of place recognition from point cloud data with a self-attention and orientation encoding network (SOE-Net) SOE-Net fully explores the relationship between points and incorporates long-range context into point-wise local descriptors. Experiments on various benchmark datasets demonstrate superior performance of the proposed network over the current state-of-the-art approaches.
  arXiv  Detail & Related papers  (2020-11-24T22:28:25Z)
• Ventral-Dorsal Neural Networks: Object Detection via Selective Attention [51.79577908317031]
  We propose a new framework called Ventral-Dorsal Networks (VDNets) Inspired by the structure of the human visual system, we propose the integration of a "Ventral Network" and a "Dorsal Network" Our experimental results reveal that the proposed method outperforms state-of-the-art object detection approaches.
  arXiv  Detail & Related papers  (2020-05-15T23:57:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.