Endow SAM with Keen Eyes: Temporal-Spatial Prompt Learning for Video Camouflaged Object Detection

The Segment Anything Model (SAM), a prompt-driven foundational model, has demonstrated remarkable performance in natural image segmentation. However, its application in video camouflaged object detection (VCOD) en-counters challenges, chiefly stemming from the overlooked temporal-spatial associations and the unreliability of user-provided prompts for camouflaged objects that are difficult to discern with the naked eye. To tackle the above issues, we endow SAM with keen eyes and propose the Temporal-spatial Prompt SAM (TSP-SAM), a novel approach tailored for VCOD via an ingenious prompted learning scheme. Firstly, motion-driven self-prompt learning is employed to capture the camouflaged object, thereby bypassing the need for user-provided prompts. With the detected subtle motion cues across consecutive video frames, the overall movement of the camouflaged object is captured for more precise spa-tial localization. Subsequently, to eliminate the prompt bias resulting from inter-frame discontinuities, the long-range consistency within the video sequences is taken into account to promote the robustness of the self-prompts. It is also injected into the encoder of SAM to enhance the representational capabilities. Extensive experimental results on two benchmarks demonstrate that the proposed TSP-SAM achieves a significant improvement over the state-of-the-art methods. With the mIoU metric increasing by 7.8% and 9.6%, TSP-SAM emerges as a groundbreaking step forward in the field of VCOD.