Green Video Camouflaged Object Detection

• URL: http://arxiv.org/abs/2501.10914v1
• Date: Sun, 19 Jan 2025 01:42:00 GMT
• Title: Green Video Camouflaged Object Detection
• Authors: Xinyu Wang, Hong-Shuo Chen, Zhiruo Zhou, Suya You, Azad M. Madni, C. -C. Jay Kuo,
• Abstract summary: We propose a green VCOD method named GreenVCOD to handle temporal information. Built upon a green ICOD method, GreenVCOD uses long- and short-term temporal neighborhoods to capture joint spatial/temporal context information. Experimental results show that GreenVCOD offers competitive performance compared to state-of-the-art VCOD benchmarks.
• Score: 28.528114525671025
• License:
• Abstract: Camouflaged object detection (COD) aims to distinguish hidden objects embedded in an environment highly similar to the object. Conventional video-based COD (VCOD) methods explicitly extract motion cues or employ complex deep learning networks to handle the temporal information, which is limited by high complexity and unstable performance. In this work, we propose a green VCOD method named GreenVCOD. Built upon a green ICOD method, GreenVCOD uses long- and short-term temporal neighborhoods (TN) to capture joint spatial/temporal context information for decision refinement. Experimental results show that GreenVCOD offers competitive performance compared to state-of-the-art VCOD benchmarks.

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