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首次观测 · 记录 #1CVF · CVPR 2026:cvf:1cb0464ccf01cc2614b9d2a50944d9f3CVF · CVPR 2026

DRAMA: Next-Gen Dynamic Orchestration for Resilient Multi-Agent Ecosystems in Flux

Zhao, Xinkui, Zhang, Yifan, Liu, Sai, Wang, Naibo, Cheng, Guanjie, Xu, Yueshen, Liu, Chang, Deng, Shuiguang 等 9 位作者

原文摘要

Embodied Multi-Agent Systems have proven highly effective in addressing complex tasks through coordinated collaboration among heterogeneous agents. However, real-world environments and task specifications are inherently dynamic, exhibiting frequent changes, uncertainty, and variability. Despite these characteristics, most existing frameworks employ static architectures with fixed agent capabilities and rigid task allocation strategies, which substantially constrain their adaptability to evolving conditions. This inflexibility presents significant challenges to maintaining robust and efficient multi-agent cooperation in dynamic and unpredictable settings.To address these limitations, we propose DRAMA, short for Dynamic Orchestration for Resilient Multi-Agent Ecosystems, tailored for rapidly changing environments. DRAMA adopts a multilayer architecture that incorporates three principal mechanisms: adaptive scheduling through an affinity-driven mechanism, fault-tolerant continuity via hierarchical trust-chain task takeover, and collective spatial intelligence that consolidates distributed observations for predictive reasoning. Together, these components enable event-triggered rescheduling and decentralized fault recovery, ensuring uninterrupted task execution amid agent arrivals, dropouts, or recoveries. Extensive experiments in the embodied VirtualHome-Social environment demonstrate that DRAMA achieves a 7% improvement in runtime efficiency and a 10% increase in throughput compared with state-of-the-art baselines, while maintaining superior stability and robustness under dynamic agent populations.

cs.CV · cs.MA

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:bb95c40397e854e436e052bc6c3f202eCVF · CVPR 2026

SegQuant: A Semantics-Aware and Generalizable Quantization Framework for Diffusion Models

Zhang, Jiaji, Sun, Ruichao, Zhao, Hailiang, Wu, Jiaju, Chen, Peng, Li, Hao, Liu, Yuying, Chow, Kingsum 等 10 位作者

原文摘要

Diffusion models have demonstrated exceptional generative capabilities but are computationally intensive, posing significant challenges for deployment in resource-constrained or latency-sensitive environments.Quantization offers an effective means to reduce model size and computational cost, with post-training quantization (PTQ) being particularly appealing due to its compatibility with pre-trained models without requiring retraining or training data.However, existing PTQ methods for diffusion models often rely on manual, architecture-specific heuristics that limit their generalizability and hinder integration with industrial deployment pipelines.To address these limitations, we propose SegQuant, a deployment-aware quantization framework that adaptively combines complementary techniques to enhance cross-model versatility.SegQuant consists of a segment-aware, graph-based quantization strategy (SegLinear) that captures structural semantics and spatial heterogeneity, along with a dual-scale quantization scheme (DualScale) that preserves polarity-asymmetric activations using a hardware-native dual-path computation, avoiding performance penalties from custom implementations, which is crucial for maintaining visual fidelity in generated outputs.SegQuant is broadly applicable beyond Transformer-based diffusion models, achieving strong performance while ensuring seamless compatibility with mainstream deployment tools.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:f6368357bbaada5d7c62960052ce627fCVF · CVPR 2026

GaussianMatch: Semi-Supervised Regression with Pseudo-Label Filtering via Multi-View Gaussian Consistency

Wang, Yin, Lu, Hao, Wang, Zixuan, Qin, Zhen, Kuang, Li, Zhou, Mengchu, Deng, Shuiguang

原文摘要

Semi-Supervised Regression (SSR) is essential in domains like sentiment analysis and healthcare where labeled data is limited but unlabeled data is plentiful. Despite its practical importance, SSR remains underexplored due to the lack of effective pseudo-labeling strategies for continuous outputs. Unlike classification, regression lacks inherent confidence measures, making it harder to filter and trust pseudo-labels. This limitation permits low-quality pseudo-labels to propagate during training without proper validation, significantly amplifying prediction errors in SSR frameworks. In this work, we propose GaussianMatch, a novel SSR framework enabling high-quality pseudo-label filtering, which selects reliable pseudo-labels through multi-view prediction consistency under feature-space smoothness assumptions. It has introduces two key innovations: 1) Gaussian Consistency Filter (GCF) that quantifies prediction consistency across weakly augmented views through Gaussian similarity scoring, retaining pseudo-labels only when all predictions fall within a confidence interval; 2) Adaptive Gaussian Standard Deviation Smoothing (AGDS) that enhances GCF's robustness through a Bayesian-regularized curriculum that phases confidence intervals from warm-up conservative bounds to progressively tightened thresholds. The use of AGDS ensures stable and reliable pseudo-label filtering throughout training. Extensive experiments demonstrate that GaussianMatch performs strongly across varying data conditions, showing notable robustness under extreme label scarcity. For instance, it outperforms the state of the art on UTKFace (30 labels), reducing error by 15.36% and improving the Coefficient of Determination by 50.21%. Our code is available at https://github.com/pywin/GaussianMatch.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗