Classification Committee for Active Deep Object Detection

by   Lei Zhao, et al.

In object detection, the cost of labeling is much high because it needs not only to confirm the categories of multiple objects in an image but also to accurately determine the bounding boxes of each object. Thus, integrating active learning into object detection will raise pretty positive significance. In this paper, we propose a classification committee for active deep object detection method by introducing a discrepancy mechanism of multiple classifiers for samples' selection when training object detectors. The model contains a main detector and a classification committee. The main detector denotes the target object detector trained from a labeled pool composed of the selected informative images. The role of the classification committee is to select the most informative images according to their uncertainty values from the view of classification, which is expected to focus more on the discrepancy and representative of instances. Specifically, they compute the uncertainty for a specified instance within the image by measuring its discrepancy output by the committee pre-trained via the proposed Maximum Classifiers Discrepancy Group Loss (MCDGL). The most informative images are finally determined by selecting the ones with many high-uncertainty instances. Besides, to mitigate the impact of interference instances, we design a Focus on Positive Instances Loss (FPIL) to make the committee the ability to automatically focus on the representative instances as well as precisely encode their discrepancies for the same instance. Experiments are conducted on Pascal VOC and COCO datasets versus some popular object detectors. And results show that our method outperforms the state-of-the-art active learning methods, which verifies the effectiveness of the proposed method.


page 1

page 2

page 3

page 7

page 8


Multiple instance active learning for object detection

Despite the substantial progress of active learning for image recognitio...

Active Learning for Object Detection with Non-Redundant Informative Sampling

Curating an informative and representative dataset is essential for enha...

Active Learning for Deep Detection Neural Networks

The cost of drawing object bounding boxes (i.e. labeling) for millions o...

SaccadeNet: A Fast and Accurate Object Detector

Object detection is an essential step towards holistic scene understandi...

KECOR: Kernel Coding Rate Maximization for Active 3D Object Detection

Achieving a reliable LiDAR-based object detector in autonomous driving i...

Computational Baby Learning

Intuitive observations show that a baby may inherently possess the capab...

Target Driven Instance Detection

While state-of-the-art general object detectors are getting better and b...

Please sign up or login with your details

Forgot password? Click here to reset