#5813. Semi-supervised Active Salient Object Detection

July 2026publication date
Proposal available till 15-05-2025
4 total number of authors per manuscript0 $

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Journal’s subject area:
Signal Processing;
Software;
Artificial Intelligence;
Computer Vision and Pattern Recognition;
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Abstract:
In this paper, we propose a novel semi-supervised active salient object detection (SOD) method that actively acquires a small subset of the most discriminative and representative samples for labeling. Two main contributions have been made to prevent the method from being overwhelmed by labeling similar distributed samples. First, we design a saliency encoder-decoder with adversarial discriminator to generate a confidence map, representing the network uncertainty on the current prediction. Then, we select the least confident (discriminative) samples from the unlabeled pool to form the “candidate labeled pool”. Second, we train a Variational Auto-Encoder (VAE) to select and add the most representative data from the “candidate labeled pool” into the labeled pool by comparing their corresponding features in the latent space.
Keywords:
Active learning; Annotation-efficient Learning; Salient object detection; Variational Auto-Encoder

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