Breaking the accuracy-resource dilemma: a lightweight adaptive video inference enhancement

Junjie Ye; Lei Huang; Peichang Zhang; Shaowu Chen; Wei Ma

arxiv: 2601.14568 · v2 · pith:6BNT6JFNnew · submitted 2026-01-21 · 💻 cs.CV · cs.AI

Breaking the accuracy-resource dilemma: a lightweight adaptive video inference enhancement

Wei Ma , Shaowu Chen , Junjie Ye , Peichang Zhang , Lei Huang This is my paper

Pith reviewed 2026-05-21 16:23 UTC · model grok-4.3

classification 💻 cs.CV cs.AI

keywords video inferencefuzzy controllermodel switchingresource efficiencyspatiotemporal correlationadaptive inferencelightweight framework

0 comments

The pith

A fuzzy controller enables real-time switching between video inference models to balance resource use and performance.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Existing video inference methods improve results by scaling up model size and complexity but often ignore the resulting resource costs on target devices. This work develops a fuzzy controller (FC-r) from system parameters and inference metrics to guide an adaptive framework. The framework uses spatiotemporal correlations between targets in adjacent video frames to switch dynamically among models of different scales according to current device resources. Experiments show the approach reaches an effective balance between resource consumption and inference quality.

Core claim

The paper establishes that a video inference enhancement framework guided by a fuzzy controller (FC-r), which accounts for key system parameters and inference-related metrics while leveraging spatiotemporal correlations of targets across adjacent frames, can dynamically switch between models of varying scales according to real-time resource conditions, thereby balancing resource utilization and inference performance.

What carries the argument

The fuzzy controller (FC-r) that determines model switches using system parameters and inference metrics, enabling adaptive scaling based on spatiotemporal video correlations.

Load-bearing premise

The fuzzy controller can reliably decide model switches without adding significant decision overhead or errors that would negate the claimed resource-performance balance.

What would settle it

Measurements on a target device showing that controller decisions cause net higher average resource use or lower accuracy than a single fixed mid-sized model would falsify the balance claim.

read the original abstract

Existing video inference (VI) enhancement methods typically aim to improve performance by scaling up model sizes and employing sophisticated network architectures. While these approaches demonstrated state-of-the-art performance, they often overlooked the trade-off of resource efficiency and inference effectiveness, leading to inefficient resource utilization and suboptimal inference performance. To address this problem, a fuzzy controller (FC-r) is developed based on key system parameters and inference-related metrics. Guided by the FC-r, a VI enhancement framework is proposed, where the spatiotemporal correlation of targets across adjacent video frames is leveraged. Given the real-time resource conditions of the target device, the framework can dynamically switch between models of varying scales during VI. Experimental results demonstrate that the proposed method effectively achieves a balance between resource utilization and inference performance.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper applies a fuzzy controller to switch video inference models dynamically using frame correlations, but the abstract gives no quantitative evidence that the controller overhead or switch errors are controlled.

read the letter

The main takeaway is a fuzzy controller (FC-r) that picks among video models of different scales in real time, guided by device resources and spatiotemporal correlations across frames. The goal is to avoid the usual accuracy-resource tradeoff on edge hardware without always running the biggest model. That combination is the concrete new piece; prior adaptive inference work exists, but this targets video specifically with fuzzy logic tied to frame-to-frame cues. The motivation is stated plainly and the framework description is straightforward, which is useful for anyone who has tried to deploy video models under tight power or latency budgets. The paper does a decent job framing why static large models waste resources and why a lightweight decision layer might help. The central claim is that experiments show the balance is achieved. That claim is hard to evaluate from what is shown. No baselines, no dataset names, no accuracy or resource numbers, and no separate measurement of the controller's own runtime or of cases where a bad switch hurt performance relative to a fixed model. The stress-test point about decision overhead and erroneous switches not being isolated is on target; without those numbers the reported aggregate gains could be overstated or even illusory. Minor implementation details like how the fuzzy rules are tuned or how often switches actually occur are also missing. This is the kind of paper that would interest people building real-time video systems for constrained devices. A reader already working on adaptive inference or edge CV could extract the high-level idea and try to reproduce the controller, but would have to fill in the experimental gaps themselves. The work shows clear enough thinking on the problem setup and is not internally contradictory, so it is worth a referee's time even if the current evidence is thin. I would send it to review and ask specifically for ablations on controller latency, switch frequency, and direct comparisons against both static baselines and other simple adaptive policies.

Referee Report

2 major / 2 minor

Summary. The paper proposes a fuzzy controller (FC-r) based on key system parameters and inference-related metrics to guide a video inference enhancement framework. The framework dynamically switches between models of varying scales by leveraging spatiotemporal correlations of targets across adjacent frames, adapting to real-time resource conditions on the target device. Experimental results are presented as demonstrating an effective balance between resource utilization and inference performance.

Significance. If the central claim holds after isolating controller overhead, the approach could offer a practical lightweight method for adaptive model selection in video inference on edge devices, extending standard fuzzy control techniques to address dynamic accuracy-resource trade-offs in computer vision pipelines.

major comments (2)

Experimental evaluation section: the reported aggregate accuracy and resource figures do not provide separate accounting of FC-r controller runtime, decision frequency, or cases of erroneous model switches relative to a static baseline. This omission is load-bearing for the central claim, as unaccounted decision overhead or errors could negate the claimed resource-accuracy balance.
Abstract and results summary: no baselines, specific metrics (e.g., mAP, latency, energy), datasets, or error bars are provided, preventing assessment of whether the balance is achieved or if post-hoc tuning occurred.

minor comments (2)

The notation and definition of the FC-r fuzzy controller parameters could be clarified with explicit membership functions or rule tables in the method section.
Figure captions and axis labels in experimental plots should explicitly state the compared methods and units for resource metrics.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment below and indicate the planned revisions to strengthen the presentation of our experimental results and claims.

read point-by-point responses

Referee: Experimental evaluation section: the reported aggregate accuracy and resource figures do not provide separate accounting of FC-r controller runtime, decision frequency, or cases of erroneous model switches relative to a static baseline. This omission is load-bearing for the central claim, as unaccounted decision overhead or errors could negate the claimed resource-accuracy balance.

Authors: We agree that providing a separate accounting of the FC-r controller overhead is necessary to fully substantiate the central claim. In the revised manuscript, we will add a new subsection to the experimental evaluation that isolates and reports the controller's runtime, decision frequency per frame, and a quantitative comparison of erroneous model switches against a static baseline. These additions will confirm that the overhead remains negligible relative to the achieved accuracy-resource gains. revision: yes
Referee: Abstract and results summary: no baselines, specific metrics (e.g., mAP, latency, energy), datasets, or error bars are provided, preventing assessment of whether the balance is achieved or if post-hoc tuning occurred.

Authors: The abstract is written as a high-level summary, but we acknowledge that greater specificity would facilitate evaluation. We will revise the abstract to explicitly reference the baselines, metrics (mAP, latency, energy), datasets, and the presence of error bars in the results. In the results section, we will also clarify that model parameters and fuzzy rules were determined via systematic cross-validation on held-out validation data rather than post-hoc adjustment on test results. revision: yes

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The paper introduces a fuzzy controller (FC-r) developed from key system parameters and inference-related metrics to enable dynamic model switching in a video inference framework that exploits spatiotemporal correlations across frames. The central result is an empirical demonstration that this adaptive approach balances resource utilization and inference performance. No equations, derivations, or self-citations are shown that reduce the claimed balance to fitted parameters by construction, self-defined quantities, or load-bearing prior work by the same authors. The method is presented as a new framework with experimental support rather than a tautological renaming or prediction forced by its own inputs, rendering the derivation self-contained.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 1 invented entities

The framework depends on an unverified fuzzy controller and the assumption that frame correlations provide useful guidance for switching; no independent evidence or code is supplied to support these.

free parameters (1)

FC-r fuzzy controller parameters
Membership functions and rules of the fuzzy controller are developed from system parameters but not specified or shown to be derived without fitting.

axioms (1)

domain assumption Spatiotemporal correlation of targets across adjacent video frames can be leveraged to guide model switching without loss of inference quality.
Explicitly stated as the basis for the VI enhancement framework.

invented entities (1)

FC-r fuzzy controller no independent evidence
purpose: To dynamically decide model scale switches based on resources and metrics.
New component introduced to address the accuracy-resource dilemma.

pith-pipeline@v0.9.0 · 5660 in / 1205 out tokens · 48398 ms · 2026-05-21T16:23:59.751466+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

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Numerous advanced video inference methods have been proposed to address var- ious challenges in the video inference (VI) process and have achieved promising results

INTRODUCTION With the deep integration of artiﬁcial intelligence (AI) in to daily life, video inference has been widely applied in vario us domains such as autonomous driving [1], video surveillance [2], and trafﬁc ﬂow monitoring [3]. Numerous advanced video inference methods have been proposed to address var- ious challenges in the video inference (VI) p...

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METHODOLOGY FC is an intelligent control paradigm that emulates human- like reasoning and decision-making using fuzzy logic [12]. To achieve self-adaptive VI, we design a FC-r capable of adapt- arXiv:2601.14568v1 [cs.CV] 21 Jan 2026 Video capture Inference Device Fuzzification Fuzzy Rule Base Fuzzy Inference Defuzzification Large Medium Small Fuzzy Contro...

work page internal anchor Pith review Pith/arXiv arXiv 2026
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EXPERIMENTS AND RESUL TS 3.1. Experiment Setup To evaluate the proposed algorithm, four scenarios were designed: inference with a single small-, medium-, or large - scale model, and the adaptive model inference with model Algorithm 1 Adaptive Model Selection for VI Require: Frame seq. F1, . . . , F n; Models {M1, . . . , M k}; Threshold K; Fuzzy rules R E...

work page 2000
[4]

Experimental results show that the resourc e utilization efﬁciency index is signiﬁcantly superior to th at of traditional single-model inference methods

CONCLUSION This paper proposes a lightweight dynamic video inference method based on fuzzy control, which effectively balances re- sources and inference performance and alleviates the dilemma between resource utilization and inference performance to a certain extent. Experimental results show that the resourc e utilization efﬁciency index is signiﬁcantly ...

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Numerous advanced video inference methods have been proposed to address var- ious challenges in the video inference (VI) process and have achieved promising results

INTRODUCTION With the deep integration of artiﬁcial intelligence (AI) in to daily life, video inference has been widely applied in vario us domains such as autonomous driving [1], video surveillance [2], and trafﬁc ﬂow monitoring [3]. Numerous advanced video inference methods have been proposed to address var- ious challenges in the video inference (VI) p...

work page

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METHODOLOGY FC is an intelligent control paradigm that emulates human- like reasoning and decision-making using fuzzy logic [12]. To achieve self-adaptive VI, we design a FC-r capable of adapt- arXiv:2601.14568v1 [cs.CV] 21 Jan 2026 Video capture Inference Device Fuzzification Fuzzy Rule Base Fuzzy Inference Defuzzification Large Medium Small Fuzzy Contro...

work page internal anchor Pith review Pith/arXiv arXiv 2026

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EXPERIMENTS AND RESUL TS 3.1. Experiment Setup To evaluate the proposed algorithm, four scenarios were designed: inference with a single small-, medium-, or large - scale model, and the adaptive model inference with model Algorithm 1 Adaptive Model Selection for VI Require: Frame seq. F1, . . . , F n; Models {M1, . . . , M k}; Threshold K; Fuzzy rules R E...

work page 2000

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Experimental results show that the resourc e utilization efﬁciency index is signiﬁcantly superior to th at of traditional single-model inference methods

CONCLUSION This paper proposes a lightweight dynamic video inference method based on fuzzy control, which effectively balances re- sources and inference performance and alleviates the dilemma between resource utilization and inference performance to a certain extent. Experimental results show that the resourc e utilization efﬁciency index is signiﬁcantly ...

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Lightweight strate- gies for decision-making of autonomous vehicles in lane change scenarios based on deep reinforcement learn- ing,

Guofa Li, Jun Y an, Yifan Qiu, Qingkun Li, Jie Li, Shengbo Eben Li, and Paul Green, “Lightweight strate- gies for decision-making of autonomous vehicles in lane change scenarios based on deep reinforcement learn- ing,” IEEE Transactions on Intelligent Transportation Systems, vol. 26, no. 5, pp. 7245–7261, 2025

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Video surveillance over wireless sensor and ac- tuator networks using active cameras,

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Real-time trafﬁc ﬂow parameter estimation from uav video based on ensemble classiﬁer and optical ﬂow,

Ruimin Ke, Zhibin Li, Jinjun Tang, Zewen Pan, and Yin- hai Wang, “Real-time trafﬁc ﬂow parameter estimation from uav video based on ensemble classiﬁer and optical ﬂow,” IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 1, pp. 54–64, 2019

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Switch: An exemplar for evaluating self- adaptive ml-enabled systems,

Arya Marda, Shubham Kulkarni, and Karthik V aid- hyanathan, “Switch: An exemplar for evaluating self- adaptive ml-enabled systems,” in Proceedings of the 19th International Symposium on Software Engineering for Adaptive and Self-Managing Systems , 2024, vol. 7, pp. 143–149

work page 2024

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Lenna: Language enhanced reasoning detection assistant,

Fei Wei, Xinyu Zhang, Ailing Zhang, Bo Zhang, and Xi- angxiang Chu, “Lenna: Language enhanced reasoning detection assistant,” in ICASSP 2025 - 2025 IEEE In- ternational Conference on Acoustics, Speech and Signal Processing (ICASSP), 2025, pp. 1–5

work page 2025

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Zs-vcos: Zero-shot outper- forms supervised video camouﬂaged object segmenta- tion,

Wenqi Guo and Shan Du, “Zs-vcos: Zero-shot outper- forms supervised video camouﬂaged object segmenta- tion,” CoRR, vol. abs/2505.01431, May 2025

work page arXiv 2025

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Hybrid multi-attention transformer for robust video object detection,

Sathishkumar Moorthy, Sachin Sakthi K.S., Sathiyamoorthi Arthanari, Jae Hoon Jeong, and Y oung Hoon Joo, “Hybrid multi-attention transformer for robust video object detection,” Engineering Appli- cations of Artiﬁcial Intelligence , vol. 139, pp. 109606, 2025

work page 2025

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Internvqa: Advancing compressed video qual- ity assessment with distilling large foundation model,

Fengbin Guan, Zihao Y u, Yiting Lu, Xin Li, and Zhibo Chen, “Internvqa: Advancing compressed video qual- ity assessment with distilling large foundation model,” in 2025 IEEE International Symposium on Circuits and Systems (ISCAS), 2025, pp. 1–5

work page 2025

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Pocket: Pruning random convolution kernels for time series classiﬁcation from a feature selection perspective,

Shaowu Chen, Weize Sun, Lei Huang, Xiao Peng Li, Qingyuan Wang, and Deepu John, “Pocket: Pruning random convolution kernels for time series classiﬁcation from a feature selection perspective,” Knowledge-Based Systems, vol. 300, pp. 112253, 2024

work page 2024

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Edgemlbalancer: A self-adaptive approach for dynamic model switching on resource- constrained edge devices,

Akhila Matathammal, Kriti Gupta, Larissa Lavanya, Ananya Vishal Halgatti, Priyanshi Gupta, and Karthik V aidhyanathan, “Edgemlbalancer: A self-adaptive approach for dynamic model switching on resource- constrained edge devices,” in 2025 IEEE 22nd Interna- tional Conference on Software Architecture Companion (ICSA-C). IEEE, 2025, pp. 543–552

work page 2025

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Towards self-adaptive machine learning- enabled systems through qos-aware model switching,

Shubham Kulkarni, Arya Marda, and Karthik V aid- hyanathan, “Towards self-adaptive machine learning- enabled systems through qos-aware model switching,” in 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE) , 2023, pp. 1721–1725

work page 2023

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“Ieee transactions on industrial electronics publica tion information,” IEEE Transactions on Industrial Elec- tronics, vol. 52, no. 2, pp. c2–c2, 2005

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Detection and tracking meet drones challenge,

Pengfei Zhu, Longyin Wen, Dawei Du, Xiao Bian, Heng Fan, Qinghua Hu, and Haibin Ling, “Detection and tracking meet drones challenge,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 44, no. 11, pp. 7380–7399, 2021

work page 2021

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Ua-detrac: A new benchmark and protocol for multi- object detection and tracking,

“Ua-detrac: A new benchmark and protocol for multi- object detection and tracking,” Computer Vision and Image Understanding, vol. 193, pp. 102907, 2020

work page 2020