Temporal-Emerged Prompting for Segment Anything in Multiframe Infrared Small Target Detection

2026-06-25

AI Summary

This work addresses the challenge of distinguishing small targets from single-frame backgrounds in infrared image sequences under extremely low signal-to-noise ratios. To this end, we propose a non-interactive segmentation method that integrates temporal motion modeling with the Segment Anything Model (SAM). By characterizing both global motion patterns and local motion deviations, our approach extracts motion discrepancy features to enhance potential target regions and, for the first time, explicitly generates temporally emergent prompts to guide SAM segmentation. This framework effectively combines large-scale semantic pretraining with task-specific temporal cues, significantly improving detection and segmentation performance for small targets in complex dynamic scenes and overcoming the performance limitations of conventional methods under ultra-low signal-to-noise conditions.

Abstract

Accurately localizing and segmenting small targets in low signal-to-noise ratio (SNR) infrared sequences remains a challenging task. Since targets are often indistinguishable from the background in individual frames, existing methods, even when equipped with advanced foundation model and powerful inter-frame association mechanisms, still fail to detect them. Motivated by the observation that targets tend to emerge gradually from the background over time and become distinguishable, we propose Temporal-Emerged Prompting for Segment Anything Model (TEP-SAM), a principled framework designed to explicitly exploit such temporal-emerged cues to modulate and prompt SAM. TEP-SAM operates by jointly modeling global motion patterns and local motion deviations to locate potential targets. It further enhances target region features by leveraging motion discrepancy, thereby generating temporal-emerged cues for SAM and enabling non-interactive segmentation. By bridging large-scale semantic pretraining with task-specific temporal modeling, TEP-SAM effectively adapts SAM to the challenging multiframe infrared small target detection task. Extensive experiments demonstrate the effectiveness of our approach, particularly under severely low-SNR conditions and in complex dynamic background.

Problem

• infrared small target detection
• low signal-to-noise ratio
• multiframe segmentation
• temporal cues
• target emergence

Innovation

• Temporal-Emerged Prompting
• Segment Anything Model
• Infrared Small Target Detection
• Motion Discrepancy
• Non-interactive Segmentation