Scoring, Remember, and Reference: Catching Camouflaged Objects in Videos

• URL: http://arxiv.org/abs/2503.17050v1
• Date: Fri, 21 Mar 2025 11:08:14 GMT
• Title: Scoring, Remember, and Reference: Catching Camouflaged Objects in Videos
• Authors: Yuang Feng, Shuyong Gao, Fuzhen Yan, Yicheng Song, Lingyi Hong, Junjie Hu, Wenqiang Zhang,
• Abstract summary: Video Camouflaged Object Detection aims to segment objects whose appearances closely resemble their surroundings.<n>Existing vision models often struggle in such scenarios due to the indistinguishable appearance of camouflaged objects.<n>We propose an end-to-end framework inspired by human memory-recognition.
• Score: 24.03405963900272
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Video Camouflaged Object Detection (VCOD) aims to segment objects whose appearances closely resemble their surroundings, posing a challenging and emerging task. Existing vision models often struggle in such scenarios due to the indistinguishable appearance of camouflaged objects and the insufficient exploitation of dynamic information in videos. To address these challenges, we propose an end-to-end VCOD framework inspired by human memory-recognition, which leverages historical video information by integrating memory reference frames for camouflaged sequence processing. Specifically, we design a dual-purpose decoder that simultaneously generates predicted masks and scores, enabling reference frame selection based on scores while introducing auxiliary supervision to enhance feature extraction.Furthermore, this study introduces a novel reference-guided multilevel asymmetric attention mechanism, effectively integrating long-term reference information with short-term motion cues for comprehensive feature extraction. By combining these modules, we develop the Scoring, Remember, and Reference (SRR) framework, which efficiently extracts information to locate targets and employs memory guidance to improve subsequent processing. With its optimized module design and effective utilization of video data, our model achieves significant performance improvements, surpassing existing approaches by 10% on benchmark datasets while requiring fewer parameters (54M) and only a single pass through the video. The code will be made publicly available.

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