SCOUT: Semi-supervised Camouflaged Object Detection by Utilizing Text and Adaptive Data Selection
This work addresses the problem of reducing annotation effort for researchers and practitioners in computer vision, specifically for camouflaged object detection, but it is incremental as it builds on existing semi-supervised frameworks.
The paper tackles the challenge of high annotation costs in Camouflaged Object Detection (COD) by proposing SCOUT, a semi-supervised method that uses text and adaptive data selection to better utilize unlabeled data, achieving state-of-the-art performance in COD benchmarks.
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.