[Paper] Hyperion: Low-Latency Ultra-HD Video Analytics via Collaborative Vision Transformer Inference

Source: arXiv - 2512.21730v1

Overview

The paper introduces Hyperion, a cloud‑device collaborative system that makes ultra‑HD video analytics with modern Vision Transformers (ViTs) fast enough for real‑time use. By smartly splitting work between an edge device and the cloud and adapting to network conditions, Hyperion cuts latency while preserving—or even boosting—accuracy, a crucial step for applications like smart surveillance, autonomous drones, and live‑stream content moderation.

Key Contributions

• Collaboration‑aware importance scorer that works at the ViT patch level to pinpoint which image regions are most critical for the downstream task.
• Dynamic scheduler that adjusts the resolution/quality of each selected patch on‑the‑fly, balancing bandwidth constraints with inference speed.
• Weighted ensembling module that fuses partial results from edge and cloud, yielding higher accuracy than either side alone.
• First end‑to‑end framework that demonstrates low‑latency, ultra‑HD ViT inference over realistic, time‑varying network conditions.
• Empirical validation showing up to 1.61× higher frame‑processing rate and +20.2% accuracy gains versus state‑of‑the‑art baselines.

Methodology

1. Patch‑level importance scoring – The edge device runs a lightweight scorer (derived from early ViT layers) to assign an “importance” weight to every 16×16 (or similar) patch of the ultra‑HD frame.
2. Selective transmission – Only the top‑k important patches are sent to the cloud. For each patch, the scheduler chooses a transmission quality (e.g., full‑resolution, down‑sampled, or compressed) based on current bandwidth and latency budgets.
3. Parallel inference –
   • Edge side: Runs a shallow ViT head on the locally retained patches, producing a quick coarse prediction.
   • Cloud side: Executes a full‑scale ViT on the received high‑importance patches, delivering a detailed prediction.
4. Weighted ensembling – The two partial outputs are merged using learned weights that reflect patch importance and confidence, producing the final result.
5. Feedback loop – Network statistics (RTT, throughput) are continuously fed back to the scheduler, enabling real‑time adaptation without human intervention.

Results & Findings

The gains hold across several network profiles (Wi‑Fi, 4G, 5G) and different ultra‑HD resolutions (4K, 8K). Ablation studies confirm that each component—importance scorer, dynamic scheduler, and weighted ensembling—contributes significantly to the overall performance boost.

Practical Implications

• Edge‑first analytics: Developers can embed a tiny ViT‑based scorer on cameras, smartphones, or IoT gateways, enabling immediate detection of critical events (e.g., safety hazards) without waiting for the cloud.
• Cost‑effective cloud usage: By transmitting only the most informative patches, bandwidth bills drop dramatically, making large‑scale deployments (city‑wide surveillance, remote drone fleets) financially viable.
• Robustness to network variability: The adaptive scheduler ensures that latency stays within real‑time bounds even when connectivity degrades, a common scenario for mobile or edge devices.
• Plug‑and‑play with existing ViTs: Hyperion works with off‑the‑shelf transformer models (e.g., ViT‑B/16, Swin‑Transformer), so teams can adopt it without retraining from scratch.
• Potential for new services: Real‑time ultra‑HD content moderation, live sports analytics, and AR/VR streaming can now leverage heavyweight vision models without sacrificing responsiveness.

Limitations & Future Work

• Scorer overhead: Although lightweight, the edge scorer still consumes CPU/GPU cycles that may be scarce on ultra‑low‑power devices.
• Patch granularity trade‑off: Fixed patch sizes may not align perfectly with object boundaries, potentially missing fine‑grained details.
• Security & privacy: Transmitting selected patches raises concerns about leaking sensitive visual information; encryption and on‑device privacy filters are not explored.
• Generalization to other modalities: The current design focuses on visual data; extending the collaborative paradigm to multimodal streams (audio‑visual, LiDAR) remains open.

Future research directions include optimizing the scorer for micro‑controllers, exploring adaptive patch shapes, integrating privacy‑preserving mechanisms, and applying the collaborative inference concept to other transformer‑based domains.

Authors

• Linyi Jiang
• Yifei Zhu
• Hao Yin
• Bo Li

Paper Information

• arXiv ID: 2512.21730v1
• Categories: cs.DC
• Published: December 25, 2025
• PDF: Download PDF