DeepAI AI Chat
Log In Sign Up

Co-occurrence matrix analysis-based semi-supervised training for object detection

02/20/2018
by   Min-Kook Choi, et al.
0

One of the most important factors in training object recognition networks using convolutional neural networks (CNNs) is the provision of annotated data accompanying human judgment. Particularly, in object detection or semantic segmentation, the annotation process requires considerable human effort. In this paper, we propose a semi-supervised learning (SSL)-based training methodology for object detection, which makes use of automatic labeling of un-annotated data by applying a network previously trained from an annotated dataset. Because an inferred label by the trained network is dependent on the learned parameters, it is often meaningless for re-training the network. To transfer a valuable inferred label to the unlabeled data, we propose a re-alignment method based on co-occurrence matrix analysis that takes into account one-hot-vector encoding of the estimated label and the correlation between the objects in the image. We used an MS-COCO detection dataset to verify the performance of the proposed SSL method and deformable neural networks (D-ConvNets) as an object detector for basic training. The performance of the existing state-of-the-art detectors (DConvNets, YOLO v2, and single shot multi-box detector (SSD)) can be improved by the proposed SSL method without using the additional model parameter or modifying the network architecture.

READ FULL TEXT
06/21/2020

Semi-Supervised Object Detection with Sparsely Annotated Dataset

In training object detector based on convolutional neural networks, sele...
07/05/2021

Semi-supervised Learning for Dense Object Detection in Retail Scenes

Retail scenes usually contain densely packed high number of objects in e...
02/01/2022

Semi-supervised 3D Object Detection via Temporal Graph Neural Networks

3D object detection plays an important role in autonomous driving and ot...
03/18/2023

Identification of Novel Classes for Improving Few-Shot Object Detection

Conventional training of deep neural networks requires a large number of...
01/12/2022

Sparsely Annotated Object Detection: A Region-based Semi-supervised Approach

Research shows a noticeable drop in performance of object detectors when...
08/03/2021

ODIP: Towards Automatic Adaptation for Object Detection by Interactive Perception

Object detection plays a deep role in visual systems by identifying inst...