Visual Resolution Router (ViR)

Updated 26 August 2025

Visual Resolution Router (ViR) is a mechanism that dynamically selects and adapts visual data resolution based on task relevance and semantic context.
It leverages adaptive strategies such as patch-level classifiers, dynamic loss ratio thresholds, and self-supervised feedback to optimize processing efficiency and maintain accuracy.
ViR implementations yield significant efficiency gains, reducing computational load and speeding up inference in applications like multimodal vision, real-time document analysis, and photonic imaging.

A Visual Resolution Router (ViR) refers to a computational or physical mechanism that dynamically selects, routes, or adapts the spatial resolution of visual data, either at the feature, token, or sensor level, to optimally balance efficiency, task performance, and fidelity. Recent years have seen the emergence of diverse ViR implementations across multimodal LLMing, computer vision backbones, optical imaging hardware, and foundation models, often incorporating adaptive or learnable routing strategies at the granularity of image patches, visual tokens, or spectral bands.

1. Conceptual Foundations of Visual Resolution Routing

The core objective of Visual Resolution Routing is to provide a flexible and context-aware pathway for selecting or compressing visual information at different resolutions based on task or semantic relevance. In the context of deep learning models, especially multimodal architectures, the ViR module adaptively determines which spatial regions or token groups require detailed preservation versus aggressive compression. This process often leverages either learned classifiers, router networks, or physically engineered microstructures. Typical motivations include reducing computational overhead, speeding up inference, and maintaining or improving accuracy by avoiding unnecessary redundancy at fine spatial scales (Wang et al., 25 Aug 2025, Lan et al., 20 Sep 2024).

2. Algorithmic Implementations in Multimodal Architectures

InternVL3.5 introduces a ViR in its "Flash" variant, where the router operates as a patch-level binary classifier. Each image is divided into patches; for each patch, ViR decides whether to retain a high-resolution representation (e.g., 256 tokens) or compress more aggressively (e.g., 64 tokens) by evaluating loss ratios:

$r_i = \frac{\mathcal{L}_{\text{ViCO}}(y_i | I_{1/16})}{\mathcal{L}_{\text{ViCO}}(y_i | I_{1/4})}$

A dynamic threshold $\tau$ determines the routing decision, such that patches with insignificant performance loss under high compression (low $r_i$ ) are routed to the more compressed representation. The ViR classifier is trained with cross-entropy loss on binary supervision generated from this ratio and thresholding scheme.

Similarly, AVG-LLaVA introduces a Visual Granularity Router (VGR) that selects among multiple visual granularities. It combines multi-scale visual tokens and instruction data via a Transformer and MLP, aggregates per-token logits with a voter mechanism, and selects the granularity that maximizes downstream LMM (large multimodal model) performance, as measured by log-probabilities. Training uses a self-supervised RGLF paradigm, where granularities are ranked via LMM feedback rather than human annotations (Lan et al., 20 Sep 2024).

3. Efficiency, Scalability, and Performance Impact

Adaptive routing of visual resolution leads to considerable increases in computational efficiency as measured by both token count reduction and inference speed. InternVL3.5 achieves up to a 50% reduction in visual token count and a 4.05× inference speedup, with minimal performance loss on tasks such as DocVQA and InfoVQA. AVG-LLaVA reports an 85.3% reduction in visual tokens and 2.53× faster inference on the AI2D benchmark (Wang et al., 25 Aug 2025, Lan et al., 20 Sep 2024).

In backbone architectures, such as HIRI-ViT, routing high-resolution features through selective “HR-branch” and “LR-branch” operations minimizes quadratic computational cost increases when input resolution is scaled up. Empirical results show that HIRI-ViT achieves a Top-1 accuracy of 84.3% on ImageNet-1K (448×448 input, ∼5 GFLOPs) versus iFormer-S’s 83.4% at 224×224, with favorable throughput and inference time (Yao et al., 18 Mar 2024).

4. Hardware and Photonic Implementations

ViR may also denote physical routers at the imaging sensor level. Pixel-scale spectral routers, such as those based on 2D Si $_3$ N $_4$ Mie-type metagratings, divide visible (VIS) and near-infrared (NIR) photons spatially between pixel sets with high efficiency, surpassing traditional color filters’ ∼50% energy utilization limit. Experimentally, these routers achieve ∼65%–82% efficiency in NIR and ∼64%–67% in VIS, yielding up to 42% signal enhancement for NIR and 30% for VIS over conventional designs. The physical routing exploits engineered multipolar scattering (ED, MD, EQ, MQ) and is robust to polarization and incident angle, with viable manufacturability at >360 nm feature sizes (Shao et al., 20 Jun 2024).

5. Practical Integration with Foundation Models and Deployment

ViR modules fit naturally into multimodal foundation model pipelines. In InternVL3.5, ViR is part of the post-finetuning stage and leverages Visual Consistency Learning (ViCO) to ensure outputs remain consistent across multiple patch resolutions; the router operates in conjunction with a pixel-shuffle module and is tightly integrated with the Decoupled Vision-Language Deployment (DvD) scheme, enabling distributed processing across GPUs and further increasing throughput. ViR’s dynamic, patch-level control is especially suited for edge-compute environments, real-time document OCR, visual reasoning agents, and high-throughput video processing (Wang et al., 25 Aug 2025).

6. Comparative Analysis and Theoretical Considerations

Distinct from fixed-resolution or globally pooled approaches, adaptive resolution routers provide fine-grained selection based on semantic or contextual information—often outperforming uniform compression strategies. Ablation studies in InternVL3.5 and AVG-LLaVA demonstrate that models equipped with ViR retain nearly 100% of baseline performance with significantly reduced computational load. Theoretical interpretations suggest that routing decisions—whether via log-probability feedback, loss ratio, or multipolar scattering optimization—implement implicit prioritization at the feature, patch, or spectral level (Wang et al., 25 Aug 2025, Lan et al., 20 Sep 2024, Shao et al., 20 Jun 2024).

7. Applications, Domain Relevance, and Future Directions

Visual Resolution Routers are especially relevant for:

Real-time visual understanding at scale (e.g., document processing, GUI interaction, video analytics)
Medical and satellite imaging, where maintaining high resolution in select regions is critical
Multimodal reasoning and embodied agency in AI agents
Advanced photonic imaging with simultaneous NIR-VIS collection

A plausible implication is that future ViR research may further unify algorithmic, architecture-level, and hardware-based routing strategies to maximize energy efficiency, dynamic range, and semantic fidelity in both computational and physical vision systems.

System	Routing Level	Efficiency Gains / Tradeoffs
InternVL3.5 (ViR-Flash)	Patch-level tokens	50% token reduction; 4.05× speedup
AVG-LLaVA (VGR)	Granularity/tokens	85.3% token reduction; 2.53× speedup
HIRI-ViT	Feature maps/stages	Maintains accuracy at high-res, lower GFLOPs
NIR-VIS Metagrating ViR	Spectral/pixel	42%–50% signal enhancement; direct NIR-VIS imaging