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Lightweight Frame Compensation for Real-Time Polyp Segmentation under Frame Skipping

Weihao Gao · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.31228 · cited by 0 works (OpenAlex) · open-access PDF

Spanish

La segmentación del polípido del segmento de salto en tiempo real en la colonoscopia asistida por ordenador se ve obstaculizada por la dificultad de implantar modelos de alta precisión bajo presupuestos de latencia estrictos. El salto del marco, que realiza inferencia en cada marco K y reutiliza o interpola el resto, mejora el rendimiento, pero introduce la desalineación del movimiento y la lesión que degrada la consistencia temporal. Para cerrar esta brecha, proponemos el Compensador del marco ligero (LFC), un módulo plug-and-play de 77K-parámetro generado por un modelo de semilla diferente, pero sin ninguna anotación de experto, insertado antes de una estructura de marco de segmentación congelada. Operando en una ventana de tres cuadros, el LFC fus

Machine translation (Meta M2M100); the English original follows.

Abstract

Real-time polyp segmentation in computer-assisted colonoscopy is hampered by the difficulty of deploying high-accuracy models under strict latency budgets. Frame skipping, which performs inference every K frames and reuses or interpolates the rest, improves throughput but introduces motion blur and lesion misalignment that degrade temporal consistency. To close this gap, we propose the Lightweight Frame Compensator (LFC), a 77K-parameter plug-and-play module inserted before a frozen segmentation backbone. Operating on a three-frame window, LFC fuses consecutive frames via spatio-temporal attention into a single compensated frame, and is trained only on pseudo-labels generated by a differently seeded model, without any expert annotation. Experiments on three polyp video benchmarks show that LFC recovers most of the accuracy degraded by direct frame skipping and, in several settings, reaches the accuracy of frame-by-frame inference, while retaining a 2.05X--2.55X throughput gain. We further evaluate the temporal stability of three backbones spanning CNN, pyramid Transformer, and global-attention Transformer architectures, revealing a spectrum of sensitivity to frame skipping that arises from how locally or globally each architecture aggregates misaligned input. The compensation gain of LFC follows this spectrum: it is largest for high-capacity Transformer backbones, which achieve the best static accuracy but suffer the most under skip-frame inference. LFC thus offers a lightweight, pluggable, and deployment-friendly route to real-time polyp segmentation.

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Topics: Computer science, Artificial intelligence, Computer vision, Segmentation, Frame (networking), Motion compensation

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