SWAN: World-Aware Adaptive Multimodal Networks for Runtime Variations

2026-04-30 · Source: cs.LG updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Emerging Technologies & Innovation · Depth: Expert, extended

Summary

SWAN (Sample and World-Aware Multimodal Network) is an adaptive multimodal deep neural network designed for real-world environments, particularly autonomous driving, that addresses runtime variations in modality quality, input complexity, and available platform resources. It features a quality-aware controller that assigns computational resources among modalities based on a user-specified maximum budget and modality Quality of Information (QoI). Within this budget, an adaptive SkipGate module further optimizes efficiency by scaling layer utilization according to sample complexity. Additionally, SWAN employs a token dropping module to mask semantically irrelevant multimodal features before object detection. Evaluated on complex multi-object 3D detection using the nuScenes dataset with simulated corruptions, SWAN reduces FLOPs by up to 49% with minimal performance degradation, outperforming baselines like ADMN and achieving competitive accuracy with fully-provisioned networks.

Key takeaway

For Computer Vision Engineers developing autonomous driving systems, SWAN offers a robust approach to managing computational resources under dynamic conditions. You should consider implementing its QoI-aware controller and adaptive gating mechanisms to maintain high detection performance while significantly reducing FLOPs and latency, especially on edge hardware like the Nvidia Jetson Orin. This can improve system efficiency and reliability in varying environmental and platform scenarios.

Key insights

SWAN adaptively manages multimodal network resources based on QoI, budget, and sample complexity for efficient real-world deployment.

Principles

• Jointly address runtime variations for robust multimodal networks.
• Prioritize high-QoI modalities under compute constraints.
• Optimize within budget via input-aware layer and token pruning.

Method

SWAN uses a NeuralSort-trained QoI-aware controller for layer allocation, a Gumbel-Sigmoid SkipGate for conditional layer execution, and a token pruning module for feature filtering, all integrated into a CMT-based AV detection framework.

In practice

• Integrate LayerDrop during network training for adaptability.
• Use NeuralSort for differentiable controller training in adaptive networks.
• Employ token pruning to reduce latency on edge devices.

Topics

• SWAN
• Adaptive Multimodal Networks
• Runtime Variations
• QoI-aware Controller
• SkipGate Module

Best for: Computer Vision Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, Robotics Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.LG updates on arXiv.org.