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Lightweight Frame Compensation for Real-Time Polyp Segmentation under Frame Skipping
Weihao Gao
TL;DR
Frame skipping can introduce prediction jitter in polyp segmentation, and temporal stability under this setting had not been systematically quantified. LFC compensates for skipped frames, recovering most lost accuracy while retaining a throughput gain; its greatest benefit is reported for high-capacity Transformer backbones.
Problem
Frame skipping can cause more severe frame-to-frame prediction jitter in Transformers, while prior work had not systematically quantified temporal stability under skipping.
Source: §1 · p. 2Method
LFC is a 77K-parameter plug-and-play module that fuses three consecutive frames into one compensated frame using spatio-temporal attention and is trained on cross-seed pseudo-labels.
Source: §1 · p. 3Results
LFC recovers most accuracy loss from frame skipping while retaining a 2.05×–2.55× throughput gain over per-frame inference, with the greatest compensation benefit for high-capacity Transformers.
Source: §1 · p. 3, §4 · pp. 13–14Takeaways & Limitations
LFC generalizes beyond its training distribution and is presented as a temporal compensation strategy applicable to other surgical endoscopic video tasks.
Source: §4 · pp. 13–14The evaluation fixes the skip-frame interval at K = 3; the effect of larger intervals is left for future work.
Source: §2.1 · p. 3
Abstract
from arXiv · showhide
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.
1 Introduction
Frame skipping can improve throughput in real-time polyp segmentation, but motion blur and lesion misalignment degrade temporal consistency. The paper proposes LFC, a lightweight frame compensator, and evaluates how backbone architecture relates to skip-frame sensitivity and compensation gains.
1 Introduction
- Frame skipping improves throughput but can introduce motion blur and lesion misalignment, degrading temporal consistency.These problems arise in segments with rapid motion, instrument occlusion, or abrupt morphological changes.Source: §1 · p. 2
- The paper identifies a gap in systematically quantifying skip-frame temporal stability across CNN, pyramid Transformer, and global-attention Transformer backbones, and in lightweight compensation for accuracy loss.Transformer misalignments can propagate globally through self-attention, whereas CNN convolution kernels provide local spatial smoothing.Source: §1 · pp. 2–3
- LFC recovers most accuracy lost to frame skipping and, in several settings, matches frame-by-frame accuracy while retaining a 2.05×–2.55× throughput gain.It is a 77K-parameter plug-and-play module trained on pseudo-labels from a differently seeded model, without expert annotation.Source: §1 · p. 3
- LFC's compensation gain depends on backbone jitter sensitivity and prediction accuracy, with high-capacity Transformers benefiting most despite stronger static accuracy.The paper evaluates three backbones spanning CNN, pyramid Transformer, and global-attention Transformer architectures.Source: §1 · p. 3
2 Method
The method applies LFC to a fixed three-frame skip interval, using the module to produce a compensated frame for a frozen segmentation backbone. Its pipeline trains and evaluates the module with cross-seed pseudo-labels, and its architecture uses pixel-level attention to combine frame features.
2.1 Problem formulation and overall framework
- With K = 3, the backbone runs once per three consecutive frames; LFC fuses the window into one compensated frame for inference without increasing backbone invocation frequency.The authors leave the effect of larger K for future work.Source: §2.1 · p. 3
2.2 Experimental pipeline
- The three-stage pipeline establishes supervised baselines, generates cross-seed pseudo-labels and skip-frame comparisons, then trains LFC with a frozen backbone and evaluates compensated inference.The third stage evaluates VIM-Polyp and HyperKvasir while retaining the once-per-three-frames schedule.Source: §2.2 · pp. 4–5
2.3 LFC architecture
- LFC has 77K parameters and combines shared frame-wise convolutional features using pixel-level spatio-temporal attention and a multi-layer fusion head.The attention weights the three frame features, while the fusion head produces the compensated frame.Source: §2.3 · p. 5
2.4 Training setup
- Training freezes the segmentation backbone and updates only LFC using Dice loss between compensated-frame predictions and pseudo-labels for the middle frame.The pseudo-labels come from a frozen frame-by-frame model; the authors state this permits deployment on existing pretrained segmentation models without retraining the backbone.Source: §2.4 · p. 6
3 Results
The results evaluate LFC across three backbone families and video datasets, reporting accuracy recovery, throughput, architectural differences in temporal stability, and component ablations under frame skipping.
3.1 Datasets and evaluation metrics
- The study uses four annotated datasets for development, VIM-Polyp for LFC training, pseudo-labeled HyperKvasir validation, and clinician-verified third-party Colon-Bench labels for external validation.Source: §3.1 · pp. 6–7
3.2 Backbone models
- ResNet-UNet, TransUNet, and Polyp-PVT span convolutional, global patch-attention, and pyramid-attention architectures; K=3 comparisons hold the inference schedule constant across fusion baselines.Source: §3.2 · pp. 7–8
3.3 Basic segmentation results
- Polyp-PVT leads static segmentation, TransUNet is competitive, and ResNet-UNet substantially underperforms both Transformer models in internal and external zero-shot testing.Source: §3.3 · pp. 8–9, Table 2 · p. 9
3.4 Main results
- Across all three backbones, LFC recovers most frame-skipping losses; it sometimes exceeds TransUNet’s FBF accuracy, matches Polyp-PVT on Colon-Bench, and yields the smallest gain on ResNet-UNet.These outcomes span pseudo-label benchmarks and third-party Colon-Bench labels.Source: §3.4 · p. 9, Table 3 · p. 10, Table 4 · p. 11
3.5 An architectural spectrum of temporal stability under frame skipping
- TransUNet shows the highest prediction jitter; ResNet-UNet’s lower jitter coincides with lower accuracy, while Polyp-PVT combines the highest FBF Dice with the lowest jitter and smaller marginal LFC gains.The reported stability measure is per-video frame-wise Dice standard deviation.Source: §3.5 · pp. 10–11, Table 7 · p. 13
3.4 Main results
- With LFC, TransUNet retains 292.8 FPS (2.55×) and Polyp-PVT retains 427.4 FPS (2.05×), while adding only 0.07M parameters—a modest cost to skip-frame throughput across backbones.Source: §3.4 · p. 10
3.6 Ablation study
- Spatial attention yields consistent gains, fusion-head benefits vary by dataset, and removing both components collapses performance; the full configuration gives the best average and a reliable cross-domain trade-off.Source: §3.6 · pp. 11–13, Table 8 · p. 13
4 Conclusion
LFC compensates skipped-frame polyp segmentation using a three-frame window and cross-seed pseudo-labels, recovering most lost accuracy while retaining a 2.05×–2.55× throughput gain. Its benefits vary across backbones with their jitter sensitivity and prediction accuracy.
4 Conclusion
- LFC fuses three consecutive frames before a frozen backbone, recovering most frame-skipping accuracy loss while retaining a 2.05×–2.55× throughput gain over per-frame inference.It is trained on cross-seed pseudo-labels, and in several settings reaches frame-by-frame accuracy.Source: §4 · p. 13
- Compensation gain depends on backbone jitter sensitivity and prediction accuracy: TransUNet is the most sensitive and benefits most, while Polyp-PVT has less room to improve and ResNet-UNet has partial inherent robustness.Global self-attention propagates misaligned patches across the feature space; the pyramid structure and convolutional smoothing are associated with lower sensitivity.Source: §4 · pp. 13–14
- LFC is presented as applicable to surgical endoscopic video tasks beyond polyp segmentation, with laparoscopic and bronchoscopic analysis and adaptive skip strategies left for future work.Source: §4 · p. 14