HP-UniIF is a unified vision framework that uses diffusion priors and a depth‑wise hierarchical conditional modulation strategy to support heterogeneous image fusion, visual restoration, and downstream perception tasks. The framework introduces task prompt modulation at bottleneck layers, a degradation prompt router at shallow layers, and an application prompt bank at decoding stages to decouple and adapt to different objectives. Experiments across multiple fusion tasks, degradations, and downstream applications show that HP‑UniIF achieves superior performance while maintaining visually faithful results and task‑relevant semantics.
arXiv:2608.21786v2 Announce Type: replace Abstract: General image fusion aims to integrate complementary information from multiple source images, but existing methods often rely on task-specific mode...
arXiv:2608. 20141v1 Announce Type: new Abstract: All-in-One Image Restoration (AiOIR) aims to handle diverse degradations within a unified model.
Infrared-visible image fusion (IVIF) is pivotal for multimodal perception, yet reconciling the inherent information disparity between thermal and textural features remains a fundamental challenge. Existing prior-guided methods often rely on static constraints that induce optimization conflicts or utilize extrinsic semantic priors from large-scale foundation models (e.
arXiv:2609.01092v1 Announce Type: new Abstract: Text-guided image fusion has recently emerged as an effective paradigm for integrating multi-modal information while enabling flexible and task-oriente...
Recent advances in diffusion models have shown impressive performance in controllable image generation and dense prediction tasks. However, existing approaches typically treat diffusion-based controllable generation and dense prediction as separate tasks, overlooking the potential benefits of jointly modeling the heterogeneous distributions.
arXiv:2608. 00111v1 Announce Type: cross Abstract: Image restoration quality can be evaluated along three complementary facets: pixel-level fidelity, human perception, and downstream machine preference.
arXiv:2505.16157v3 Announce Type: replace Abstract: Transformer-based models have made remarkable progress in image restoration (IR) tasks. However, the quadratic complexity of self-attention in Tran...
arXiv:2608.29220v1 Announce Type: new Abstract: Multimodal image fusion (MMIF) aims to integrate complementary sensor data into a single representation that preserves intrinsic scene reality while el...
arXiv:2608. 10544v1 Announce Type: cross Abstract: Image restoration is fundamentally constrained by the tradeoff between distortion and perception: minimizing pixel-wise error yields over-smoothed results, whereas optimizing for perceptual realism often introduces structural deviations.
arXiv:2606. 01503v1 Announce Type: cross Abstract: Unified vision-language models (VLMs) integrate visual understanding and visual generation within a single autoregressive backbone, but their joint training is computationally expensive and largely overlooked from an efficiency perspective.
Controllable infrared-visible image fusion aims to integrate complementary thermal and structural information with flexible region-aware modulation, producing fused images that adapt to diverse user requirements and downstream tasks. However, existing methods typically rely on predefined discrete control conditions, leading to a sparse space that fails to support fine-grained modulation demands.
CrossMambaTuning is a new framework that adapts pretrained learned image compression models to machine vision tasks with minimal retraining. It combines State Space Models with cross‑layer interaction, featuring a Mamba adapter that uses task‑specific prompts and multi‑scale branching, and a Scale‑Invariant Cross‑Layer Adapter (SICA) that shares parameters across scales to reduce redundancy. Experiments show that this approach achieves state‑of‑the‑art performance while cutting parameter overhead by 72% compared to existing methods.