Semi-Supervised Object Detection with Sparsely Annotated Dataset

• URL: http://arxiv.org/abs/2006.11692v1
• Date: Sun, 21 Jun 2020 02:26:48 GMT
• Title: Semi-Supervised Object Detection with Sparsely Annotated Dataset
• Authors: Jihun Yoon, Seungbum Hong, Sanha Jeong, Min-Kook Choi
• Abstract summary: In training object detector based on convolutional neural networks, selection of effective positive examples for training is an important factor. We used two approaches to solve this problem: 1) the use of an anchorless object detector and 2) a semi-supervised learning-based object detection using a single object tracker. We were able to achieve textbfrunner-up performance in the Unseen section while achieving the first place in the Seen section of the Epic-Kitchens 2020 object detection challenge under IoU > 0.5 evaluation.
• Score: 0.27719338074999533
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In training object detector based on convolutional neural networks, selection of effective positive examples for training is an important factor. However, when training an anchor-based detectors with sparse annotations on an image, effort to find effective positive examples can hinder training performance. When using the anchor-based training for the ground truth bounding box to collect positive examples under given IoU, it is often possible to include objects from other classes in the current training class, or objects that are needed to be trained can only be sampled as negative examples. We used two approaches to solve this problem: 1) the use of an anchorless object detector and 2) a semi-supervised learning-based object detection using a single object tracker. The proposed technique performs single object tracking by using the sparsely annotated bounding box as an anchor in the temporal domain for successive frames. From the tracking results, dense annotations for training images were generated in an automated manner and used for training the object detector. We applied the proposed single object tracking-based semi-supervised learning to the Epic-Kitchens dataset. As a result, we were able to achieve \textbf{runner-up} performance in the Unseen section while achieving the first place in the Seen section of the Epic-Kitchens 2020 object detection challenge under IoU > 0.5 evaluation

Related papers

• Proposal-Contrastive Pretraining for Object Detection from Fewer Data [11.416621957617334]
  We present Proposal Selection Contrast (ProSeCo), a novel unsupervised overall pretraining approach. ProSeCo uses the large number of object proposals generated by the detector for contrastive learning. We show that our method outperforms state of the art in unsupervised pretraining for object detection on standard and novel benchmarks.
  arXiv  Detail & Related papers  (2023-10-25T17:59:26Z)
• Improved Region Proposal Network for Enhanced Few-Shot Object Detection [23.871860648919593]
  Few-shot object detection (FSOD) methods have emerged as a solution to the limitations of classic object detection approaches. We develop a semi-supervised algorithm to detect and then utilize unlabeled novel objects as positive samples during the FSOD training stage. Our improved hierarchical sampling strategy for the region proposal network (RPN) also boosts the perception of the object detection model for large objects.
  arXiv  Detail & Related papers  (2023-08-15T02:35:59Z)
• ALSO: Automotive Lidar Self-supervision by Occupancy estimation [70.70557577874155]
  We propose a new self-supervised method for pre-training the backbone of deep perception models operating on point clouds. The core idea is to train the model on a pretext task which is the reconstruction of the surface on which the 3D points are sampled. The intuition is that if the network is able to reconstruct the scene surface, given only sparse input points, then it probably also captures some fragments of semantic information.
  arXiv  Detail & Related papers  (2022-12-12T13:10:19Z)
• Label-Efficient Object Detection via Region Proposal Network Pre-Training [58.50615557874024]
  We propose a simple pretext task that provides an effective pre-training for the region proposal network (RPN) In comparison with multi-stage detectors without RPN pre-training, our approach is able to consistently improve downstream task performance.
  arXiv  Detail & Related papers  (2022-11-16T16:28:18Z)
• Robust Object Detection in Remote Sensing Imagery with Noisy and Sparse Geo-Annotations (Full Version) [4.493174773769076]
  In this paper, we present a novel approach for training object detectors with extremely noisy and incomplete annotations. Our method is based on a teacher-student learning framework and a correction module accounting for imprecise and missing annotations. We demonstrate that our approach improves standard detectors by 37.1% $AP_50$ on a noisy real-world remote-sensing dataset.
  arXiv  Detail & Related papers  (2022-10-24T07:25:31Z)
• Label-Free Synthetic Pretraining of Object Detectors [67.17371526567325]
  We propose a new approach, Synthetic optimized layout with Instance Detection (SOLID), to pretrain object detectors with synthetic images. Our "SOLID" approach consists of two main components: (1) generating synthetic images using a collection of unlabelled 3D models with optimized scene arrangement; (2) pretraining an object detector on "instance detection" task. Our approach does not need any semantic labels for pretraining and allows the use of arbitrary, diverse 3D models.
  arXiv  Detail & Related papers  (2022-08-08T16:55:17Z)
• Aligning Pretraining for Detection via Object-Level Contrastive Learning [57.845286545603415]
  Image-level contrastive representation learning has proven to be highly effective as a generic model for transfer learning. We argue that this could be sub-optimal and thus advocate a design principle which encourages alignment between the self-supervised pretext task and the downstream task. Our method, called Selective Object COntrastive learning (SoCo), achieves state-of-the-art results for transfer performance on COCO detection.
  arXiv  Detail & Related papers  (2021-06-04T17:59:52Z)
• DAP: Detection-Aware Pre-training with Weak Supervision [37.336674323981285]
  This paper presents a detection-aware pre-training (DAP) approach for object detection tasks. We transform a classification dataset into a detection dataset through a weakly supervised object localization method based on Class Activation Maps. We show that DAP can outperform the traditional classification pre-training in terms of both sample efficiency and convergence speed in downstream detection tasks including VOC and COCO.
  arXiv  Detail & Related papers  (2021-03-30T19:48:30Z)
• Data Augmentation for Object Detection via Differentiable Neural Rendering [71.00447761415388]
  It is challenging to train a robust object detector when annotated data is scarce. Existing approaches to tackle this problem include semi-supervised learning that interpolates labeled data from unlabeled data. We introduce an offline data augmentation method for object detection, which semantically interpolates the training data with novel views.
  arXiv  Detail & Related papers  (2021-03-04T06:31:06Z)
• One-Shot Object Detection without Fine-Tuning [62.39210447209698]
  We introduce a two-stage model consisting of a first stage Matching-FCOS network and a second stage Structure-Aware Relation Module. We also propose novel training strategies that effectively improve detection performance. Our method exceeds the state-of-the-art one-shot performance consistently on multiple datasets.
  arXiv  Detail & Related papers  (2020-05-08T01:59:23Z)

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.