GiraffeDet: A Heavy-Neck Paradigm for Object Detection

• URL: http://arxiv.org/abs/2202.04256v1
• Date: Wed, 9 Feb 2022 03:23:49 GMT
• Title: GiraffeDet: A Heavy-Neck Paradigm for Object Detection
• Authors: Yiqi Jiang, Zhiyu Tan, Junyan Wang, Xiuyu Sun, Ming Lin, Hao Li
• Abstract summary: We propose a novel heavy-neck paradigm, GiraffeDet, for efficient object detection. GiraffeDet uses an extremely lightweight backbone and a very deep and large neck module. GiraffeDet consistently outperforms previous SOTA models across a wide spectrum of resource constraints.
• Score: 13.476168814994862
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In conventional object detection frameworks, a backbone body inherited from image recognition models extracts deep latent features and then a neck module fuses these latent features to capture information at different scales. As the resolution in object detection is much larger than in image recognition, the computational cost of the backbone often dominates the total inference cost. This heavy-backbone design paradigm is mostly due to the historical legacy when transferring image recognition models to object detection rather than an end-to-end optimized design for object detection. In this work, we show that such paradigm indeed leads to sub-optimal object detection models. To this end, we propose a novel heavy-neck paradigm, GiraffeDet, a giraffe-like network for efficient object detection. The GiraffeDet uses an extremely lightweight backbone and a very deep and large neck module which encourages dense information exchange among different spatial scales as well as different levels of latent semantics simultaneously. This design paradigm allows detectors to process the high-level semantic information and low-level spatial information at the same priority even in the early stage of the network, making it more effective in detection tasks. Numerical evaluations on multiple popular object detection benchmarks show that GiraffeDet consistently outperforms previous SOTA models across a wide spectrum of resource constraints.

Related papers

• Innovative Horizons in Aerial Imagery: LSKNet Meets DiffusionDet for Advanced Object Detection [55.2480439325792]
  We present an in-depth evaluation of an object detection model that integrates the LSKNet backbone with the DiffusionDet head. The proposed model achieves a mean average precision (MAP) of approximately 45.7%, which is a significant improvement. This advancement underscores the effectiveness of the proposed modifications and sets a new benchmark in aerial image analysis.
  arXiv  Detail & Related papers  (2023-11-21T19:49:13Z)
• Unveiling Camouflage: A Learnable Fourier-based Augmentation for Camouflaged Object Detection and Instance Segmentation [27.41886911999097]
  We propose a learnable augmentation method for camouflaged object detection (COD) and camouflaged instance segmentation (CIS) Our proposed augmentation method boosts the performance of camouflaged object detectors and camouflaged instance segmenters by large margins.
  arXiv  Detail & Related papers  (2023-08-29T22:43:46Z)
• Frequency Perception Network for Camouflaged Object Detection [51.26386921922031]
  We propose a novel learnable and separable frequency perception mechanism driven by the semantic hierarchy in the frequency domain. Our entire network adopts a two-stage model, including a frequency-guided coarse localization stage and a detail-preserving fine localization stage. Compared with the currently existing models, our proposed method achieves competitive performance in three popular benchmark datasets.
  arXiv  Detail & Related papers  (2023-08-17T11:30:46Z)
• Fast and Accurate Object Detection on Asymmetrical Receptive Field [0.0]
  This article proposes methods for improving object detection accuracy from the perspective of changing receptive fields. The structure of the head part of YOLOv5 is modified by adding asymmetrical pooling layers. The performances of the new model in this article are compared with original YOLOv5 model and analyzed from several parameters.
  arXiv  Detail & Related papers  (2023-03-15T23:59:18Z)
• Adaptive Rotated Convolution for Rotated Object Detection [96.94590550217718]
  We present Adaptive Rotated Convolution (ARC) module to handle rotated object detection problem. In our ARC module, the convolution kernels rotate adaptively to extract object features with varying orientations in different images. The proposed approach achieves state-of-the-art performance on the DOTA dataset with 81.77% mAP.
  arXiv  Detail & Related papers  (2023-03-14T11:53:12Z)
• Rethinking the Detection Head Configuration for Traffic Object Detection [11.526701794026641]
  We propose a lightweight traffic object detection network based on matching between detection head and object distribution. The proposed model achieves more competitive performance than other models on BDD100K dataset and our proposed ETFOD-v2 dataset.
  arXiv  Detail & Related papers  (2022-10-08T02:23:57Z)
• High-resolution Iterative Feedback Network for Camouflaged Object Detection [128.893782016078]
  Spotting camouflaged objects that are visually assimilated into the background is tricky for object detection algorithms. We aim to extract the high-resolution texture details to avoid the detail degradation that causes blurred vision in edges and boundaries. We introduce a novel HitNet to refine the low-resolution representations by high-resolution features in an iterative feedback manner.
  arXiv  Detail & Related papers  (2022-03-22T11:20:21Z)
• Pretrained equivariant features improve unsupervised landmark discovery [69.02115180674885]
  We formulate a two-step unsupervised approach that overcomes this challenge by first learning powerful pixel-based features. Our method produces state-of-the-art results in several challenging landmark detection datasets.
  arXiv  Detail & Related papers  (2021-04-07T05:42:11Z)
• Slender Object Detection: Diagnoses and Improvements [74.40792217534]
  In this paper, we are concerned with the detection of a particular type of objects with extreme aspect ratios, namely textbfslender objects. For a classical object detection method, a drastic drop of $18.9%$ mAP on COCO is observed, if solely evaluated on slender objects.
  arXiv  Detail & Related papers  (2020-11-17T09:39:42Z)
• Few-shot Object Detection on Remote Sensing Images [11.40135025181393]
  We introduce a few-shot learning-based method for object detection on remote sensing images. We build our few-shot object detection model upon YOLOv3 architecture and develop a multi-scale object detection framework.
  arXiv  Detail & Related papers  (2020-06-14T07:18:10Z)
• Real Time Multi-Class Object Detection and Recognition Using Vision Augmentation Algorithm [0.0]
  We introduce a novel real time detection algorithm which employs upsampling and skip connection to extract multiscale features at different convolution levels in a learning task. The detection precision of the model is shown to be higher and faster than that of the state-of-the-art models.
  arXiv  Detail & Related papers  (2020-03-17T01:08:24Z)

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.