Confidence-Aware Learning for Camouflaged Object Detection

06/22/2021
by   Jiawei Liu, et al.
0

Confidence-aware learning is proven as an effective solution to prevent networks becoming overconfident. We present a confidence-aware camouflaged object detection framework using dynamic supervision to produce both accurate camouflage map and meaningful "confidence" representing model awareness about the current prediction. A camouflaged object detection network is designed to produce our camouflage prediction. Then, we concatenate it with the input image and feed it to the confidence estimation network to produce an one channel confidence map.We generate dynamic supervision for the confidence estimation network, representing the agreement of camouflage prediction with the ground truth camouflage map. With the produced confidence map, we introduce confidence-aware learning with the confidence map as guidance to pay more attention to the hard/low-confidence pixels in the loss function. We claim that, once trained, our confidence estimation network can evaluate pixel-wise accuracy of the prediction without relying on the ground truth camouflage map. Extensive results on four camouflaged object detection testing datasets illustrate the superior performance of the proposed model in explaining the camouflage prediction.

READ FULL TEXT

page 1

page 3

page 6

page 7

research
12/10/2020

Uncertainty-Aware Deep Calibrated Salient Object Detection

Existing deep neural network based salient object detection (SOD) method...
research
09/27/2020

Adaptive confidence thresholding for semi-supervised monocular depth estimation

Self-supervised monocular depth estimation has become an appealing solut...
research
09/03/2018

Learning Saliency Prediction From Sparse Fixation Pixel Map

Ground truth for saliency prediction datasets consists of two types of m...
research
07/01/2013

An Empirical Study into Annotator Agreement, Ground Truth Estimation, and Algorithm Evaluation

Although agreement between annotators has been studied in the past from ...
research
05/26/2022

Penalizing Proposals using Classifiers for Semi-Supervised Object Detection

Obtaining gold standard annotated data for object detection is often cos...
research
03/30/2016

Confidence driven TGV fusion

We introduce a novel model for spatially varying variational data fusion...
research
07/27/2021

Is Object Detection Necessary for Human-Object Interaction Recognition?

This paper revisits human-object interaction (HOI) recognition at image ...

Please sign up or login with your details

Forgot password? Click here to reset