SCOUT: Semi-supervised Camouflaged Object Detection by Utilizing Text and Adaptive Data Selection

TL;DR

SCOUT introduces a semi-supervised approach for camouflaged object detection that effectively utilizes unlabeled data through adaptive selection and text-visual interaction, achieving state-of-the-art results.

Contribution

The paper proposes novel modules for adaptive data selection and text fusion, improving semi-supervised camouflaged object detection performance.

Findings

Outperforms previous semi-supervised methods in COD

Achieves state-of-the-art performance on new dataset

Demonstrates effective use of unlabeled data

Abstract

The difficulty of pixel-level annotation has significantly hindered the development of the Camouflaged Object Detection (COD) field. To save on annotation costs, previous works leverage the semi-supervised COD framework that relies on a small number of labeled data and a large volume of unlabeled data. We argue that there is still significant room for improvement in the effective utilization of unlabeled data. To this end, we introduce a Semi-supervised Camouflaged Object Detection by Utilizing Text and Adaptive Data Selection (SCOUT). It includes an Adaptive Data Augment and Selection (ADAS) module and a Text Fusion Module (TFM). The ADSA module selects valuable data for annotation through an adversarial augment and sampling strategy. The TFM module further leverages the selected valuable data by combining camouflage-related knowledge and text-visual interaction. To adapt to this work, we build a new dataset, namely RefTextCOD. Extensive experiments show that the proposed method surpasses previous semi-supervised methods in the COD field and achieves state-of-the-art performance. Our code will be released at https://github.com/Heartfirey/SCOUT.