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首次观测 · 记录 #1CVF · CVPR 2026:cvf:1f40eccc48dd89e3b72c9d65b89edcfbCVF · CVPR 2026

BiProLoRA: Bilevel Prompt LoRA for Real Scene Recovery

An, Nan, Ma, Long, Ma, Tengyu, Liu, Zhu, Liu, Yingchi, Liu, Risheng

原文摘要

The emergence of large generative models has substantially advanced learning-based scene recovery in the synthetic domain. However, these models generalize poorly to real scenarios stemming from the significant distribution gap, alongside poor adaptation to complex and unforeseen degradations. Consequently, it is imperative to develop a real scene adaptation strategy that yields faithful restorations with reliable generalizability. To this end, we propose Bilevel Prompt LoRA, a novel learning paradigm designed to effectively adapt pre-trained generative models for real scene recovery. First, we introduce a self-supervised distribution-fidelity learning scheme to calibrate the autoencoding pathway under task-irrelevant real distributions to improve texture fidelity. Subsequently, a bilevel joint modeling via hyperparameter optimization is further established, empowering robust synthetic-to-real adaptation for both seen and unseen scenes by exploiting the complementary advantages between LoRA and Prompts to foster mutual promotion. Extensive evaluations on diverse real adverse scenarios demonstrate our superiority, with comprehensive algorithm analyses proving our effectiveness.

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:834c57116aa3b1a5659be7f2ba64e436CVF · CVPR 2026

Streaming Diffusion Model for Fast Infrared and Visible Video Fusion

Liu, Jinyuan, Sun, Ludan, Ma, Tengyu, Yang, Chunyan, Jiang, Zhiying, Ma, Long, Liu, Risheng, Fan, Xin

原文摘要

Infrared and visible video fusion is pivotal for robust perceptual systems, aiming to synthesize a comprehensive video stream that leverages both thermal resilience and textured details. However, prevailing methods, by treating videos as sequences of independent frames, inherently introduce temporal incoherence, such as flickering and ghosting artifacts. While diffusion models possess strong generative priors to remedy this, their iterative nature is prohibitively slow for video. To resolve this fundamental dilemma, we propose a streaming diffusion model for efficient infrared and visible video fusion, termed SDMFusion. Our key insight is to exploit the generative prior of a pre-trained diffusion model into a one-step sampling framework, while explicitly modeling temporal dynamics. We design a memory-augmented latent pipeline where a temporal aggregation adapter aligns and propagates cross-frame features to ensure coherence, supported by a dedicated temporal consistency loss. This approach effectively decouples the challenge of achieving high fidelity from maintaining temporal stability. Extensive experiments on four benchmarks demonstrate that our method establishes a new state-of-the-art, generating fused videos with exceptional spatio-temporal consistency at a speed suitable for real-time application. The code is available at https://github.com/DandanYoung/SDMFusion.

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:c54cda0989e3e8354f507ed085dde4afCVF · CVPR 2026

Human-Centric Multi-Exposure Fusion: Benchmark and Bi-level Cognition Distillation Framework

Shang, Jingjie, Ma, Tengyu, Zhang, Heng, Liu, Jinyuan, Liu, Risheng, Wang, Yuan, Bo, Xiaochen

原文摘要

Multi-Exposure Fusion (MEF) seeks to generate a single high-quality image from multiple inputs captured at different exposure levels. Despite substantial progress, most existing approaches depend on statistical metrics that poorly reflect human perceptual preferences. Electroencephalography (EEG) provides a direct physiological window into human cognition, yet its use in low-level vision remains limited due to scarce paired data and the absence of bio-signals during inference. We address these challenges through two key contributions. First, we introduce Cog-Expo, the first dataset capturing human cognitive responses to multi-exposure stimuli, establishing a bridge between neuroscience and computational photography. Second, we propose a bi-level coupled learning framework that leverages this cognitive information without requiring it during inference. A Mental Integrated Transformer serves as the Teacher, incorporating cognitive priors to guide visual feature learning, while a lightweight Student is trained to approximate these cues using only image inputs. Through bi-level optimization, the Teacher learns inherently distillable representations, enabling the Student to emulate cognitive guidance efficiently. Extensive experiments confirm that our method achieves state-of-the-art fusion performance and aligns more closely with human perception.

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:d0baf2ed80614d6adf0d6799443575e7CVF · CVPR 2026

Taming Generative Diffusion Model for Task-Oriented Infrared Imaging

Ma, Tengyu, Dai, Zhilong, Diao, Yubo, An, Guanming, Ma, Long, Liu, Jinyuan, Liu, Risheng

原文摘要

Infrared imaging is essential for perception in harsh environments. However, dynamically coupled degradation factors severely impair visual quality and downstream semantic accuracy. Although generative diffusion models provide strong image restoration priors, high computational cost and physical inconsistency limit their application in infrared sensing. To bridge these gaps, we reformulate infrared imaging as a single-step diffusion process, aligning degraded observations with trajectory latent states via dynamic timestep estimation to leverage timestep-specific diffusion priors for high-fidelity reconstruction. Meanwhile, we introduce a spectral regularization term to enforce thermal radiation constraints and ensure physical consistency. Subsequently, a task-aware low-rank adaptation mechanism is devised through dynamic prompting to enable efficient transfer across downstream infrared tasks. Experiments demonstrate our method surpasses existing approaches in restoration quality, semantic structure preservation, and task generalization. The code is available at https://github.com/csmty/InfraredIR.

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 ↗