arXiv subjects

Yifei Qu

Publications and source records attributed to Yifei Qu.

3 recordsLinked to original sources

arXiv2026

Efficient Real-World Dehazing via Physics-Inspired Global-Local Decoupling

Real-world single image dehazing is highly ill-posed due to spatially and spectrally varying scattering, while practical deployment demands lightweight and low-latency models. Existing approaches either rely on fragile physical inversion under simplified assumptions or adopt heavy blind architectures unsuitable for edge deployment. To overcome these limitations, we propose PGL-Net (Physics-Inspired Global-Local Decoupling Network), a lightweight framework that incorporates physical inductive biases via operator-level emulation, avoiding explicit parameter estimation. It decouples dehazing into global distribution rectification and local structural refinement. A Physics-Inspired Affine Fusion (PAF) module performs globally conditioned alignment across hierarchical skip connections to compensate for haze-induced bias, while a compact Degradation-Aware Modulation (DAM) block adaptively restores spatially and spectrally variant details through dynamic feature modulation. Extensive experiments on multiple real-world benchmarks demonstrate that PGL-Net achieves state-of-the-art restoration quality with significantly reduced complexity. Compared with the recent SOTA SGDN, the Tiny variant (PGL-Net-T) improves PSNR by up to 2.6dB and consistently enhances downstream object detection accuracy, while achieving over a 10x reduction in inference latency. Code is available at https://github.com/sc-30-bit/PGL-Net}{github.com/sc-30-bit/PGL-Net.

cs.CV↗

arXiv2026

AcupunctureBench: A Multimodal Benchmark for Evaluating Generative Models in the Acupuncture Domain

As large language models (LLMs) are increasingly applied in the field of traditional Chinese medicine (TCM), standardized benchmark evaluations for TCM have gradually emerged. Acupuncture is a crucial component of TCM. However, existing evaluation benchmarks are lacking in this domain. To address this gap, this paper proposes AcupunctureBench, the first independently constructed large-scale multimodal benchmark for the acupuncture domain, covering text, image, and video modalities. The AcupunctureBench spans multidimen sional knowledge in acupuncture, including acupuncture theory, meridians and acupoints, clinical reasoning, TCM syndromes, and practical acupoint operation. To achieve large-scale construction while ensuring medical professionalism, this work designs a human-machine collaborative data construction pipeline and proposes efficient deduplication strategies to reduce redundant samples, while extensively adopting data synthesis to mitigate data leak age risks. AcupunctureBench contains a total of 58,526 question samples and 1,216 video samples, covering multiple task types including objective questions, subjective question answering, case analysis, image understanding, image reasoning, and video understanding. Based on this benchmark, this paper evaluates 14 large language models, 14 large multimodal models, and 4 video foundation models, and further releases the first dedicated evaluation website for the acupuncture domain.

q-bio.QM↗

arXiv2026

SCOPE-Router: Cost-Aware Open-Set VLM Routing for Execution-Oriented Tasks

Model routing aims to select the most suitable model from a candidate pool for each query, balancing quality and cost. Existing VLM routing research is limited to traditional VQA evaluation, lacks systematic calibration optimization for open-set scenarios, and employs training objectives that dilute multi-positive signals via softmax normalization without incorporating cost. We address these limitations with three contributions: (1)VLM-ExecRouterBench, the first execution-oriented VLM routing benchmark covering Code, Agentic, and Search domains with 11 candidate models spanning nearly two orders of magnitude in pricing; (2)SCOPE-Router, a dual-tower router that matches queries to model behavior profiles constructed via hybrid calibration (random/diagnostic/diversity sampling), enabling new models to join routing without retraining; (3)CRM+RCCR, an architecture-agnostic cost-aware objective that encodes cost preference into continuous relevance targets through per-pair independent scoring, eliminating multi-positive dilution while regularizing queries with similar routing preferences to be closer in the routing space. Empirically, SCOPE-Router achieves the best Rank Score on all three benchmarks, surpassing the runner-up by 1.84 points under OOD settings and by 6.75 points under doubly OOD open-set evaluation. When applied to four diverse routers, CRM+RCCR improves Rank Score by 1.25--6.21 points.

cs.CV↗